Disclaimer: The purpose of the Open Case Studies project is to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts.

Motivation

This case study explores how different countries have contributed to Carbon Dioxide (CO2) emissions over time and how CO2 emission rates may relate to increasing global temperatures and increased rates of natural disasters and storms. This report provides a basis for the motivation: https://www.epa.gov/report-environment/greenhouse-gases.

CO2 makes up the largest proportion of greenhouse gas emissions in the United States:

A variety of sources and sectors contribute to greenhouse gas emissions, with transportation contributing the most metric tons of CO2:

So why should we pay attention to greenhouse gases?

According to the US Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks 2020 Report:

Greenhouse gases absorb infrared radiation, thereby trapping heat in the atmosphere and making the planet warmer. The most important greenhouse gases directly emitted by humans include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and several fluorine-containing halogenated substances. Although CO2, CH4, and N2O occur naturally in the atmosphere, human activities have changed their atmospheric concentrations. From the pre- industrial era (i.e., ending about 1750) to 2018, concentrations of these greenhouse gases have increased globally by 46, 165, and 23 percent, respectively (IPCC 2013; NOAA/ESRL 2019a, 2019b, 2019c).

  • IPCC stands for the Intergovernmental Panel on Climate Change

There are many signs that our planet is experiencing warmer temperatures:

The connection between greenhouse gas levels and global temperatures and the influence of increased global temperatures on human health are motivated by these reports:

Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program.

  1. “Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2018.” EPA 430-R-20-002, Tech. Rep. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.

The National Climate Assessment Report states that:

Heat-trapping gases already in the atmosphere have committed us to a hotter future with more climate-related impacts over the next few decades. The magnitude of climate change beyond the next few decades depends primarily on the amount of heat-trapping gases that human activities emit globally, now and in the future.

See here and here for more information.

Main Questions

Our main question:

  1. How have global CO2 emission rates changed over time? In particular for the US, and how does the US compare to other countries?
  2. Are US CO2 emissions, global temperatures, and US storm rates associated?

Learning Objectives

In this case study, we will explore CO2 emission data from around the world. We will also focus on the US specifically to evaluate patterns of temperatures and storm activity. This case study will particularly focus on how to use different datasets that span different ranges of time, as well as how to create visualizations of patterns over time. We will especially focus on using packages and functions from the Tidyverse, such as dplyr, tidyr, plotlyand gganimate. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.

We will begin by loading the packages that we will need:

Package Use
here to easily load and save data
readxl to import the excel file data
readr to import the csv file data
dplyr to view and wrangle the data
magrittr to use and reassign data objects using the %<>%pipe operator
tidyverse to wrangle the data and create ggplot2 plots
plotyly to make the visualizations
ggplot2 to make visualizations
ggrepel to add labels that don’t overlap to plots
gganimate to make the plots interactive
RColorBrewer to have greater control over the color in our plots

The first time we use a function, we will use the :: to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.

Context

Greenhouse gas emissions are due to both natural processes and anthropogenic (human-derived) activities.

These emissions are one of the contributing factors to rising global temperatures, which can have a great influence on public health as illustrated in the following image:

Gases in the atmosphere can contribute to climate change both directly and indirectly. Direct effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or albedo). The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of a greenhouse gas to trap heat in the atmosphere relative to another gas. The GWP of a greenhouse gas is defined as the ratio of the accumulated radiative forcing within a specific time horizon caused by emitting 1 kilogram of the gas, relative to that of the reference gas CO2 (IPCC 2013). Therefore GWP-weighted emissions are provided in million metric tons of CO2 equivalent (MMT CO2 Eq.)

CO2 is actually the least capable of the greenhouse gases for trapping heat:

However, because CO2 is so much more abundant and stays in the atmosphere so much longer than other greenhouse gases, it has been the largest contributor to global warming.

See here for more details.

Furthermore, sizing CO2 levels also influence ocean acidity:

This makes it difficult for organisms to maintain their shells or skeletons that are made of calcium carbonate, thus making it more difficult for these organisms to survive and impacting their role in the ecosystem and food chain.

Furthermore, greenhouse gas emissions are believed to influence storm rates.

Indeed events with high levels of precipitation which can induce flooding and property damage are generally increasing around the country:

Limitations

There are some important considerations regarding this data analysis to keep in mind:

  1. The datasets included only include countries and years in which countries were reporting such information to the agencies that collected the data. Thus the data is incomplete. For example while we have a fairly good sense of CO2 emissions globally for later years, additional emissions were also produced by countries that are not included in the data.

  2. Correlation or association does not imply causation. We will be showing how different datasets show similar trends across time. This does not imply that one caused the other. However, in the case of some of the data we will show, there is additional scientific evidence to suggest that for example, increased CO2 emissions may cause increased temperatures or increased rates of disastors. However, simply showing a similar trend over time does not in itself prove that two variables are causally related. As you can see from this plot, often data may show a similar pattern over time by random chance. See this website for more examples.

What are the data?

In this case study we will be using data related to CO2 emissions, as well as other data that may influence, be influenced or relate to CO2 emissions. Most of our data was obtained from Gapminder, which is a unique nonprofit that provides a variety of data for free.

In their words, Gapminder is…

Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.

The data that we will be using from Gapminder was obtained from the World Bank.

In addition we will use some data that is specific to the United States from the [National Oceanic and Atmospheric Administration (NOAA)] (https://www.noaa.gov/), which is an agency that collects weather and climate data.

Data Time span Source Orginal Source Description Citation
CO2 emissions 1751 to 2014 Gapminder Carbon Dioxid Information Analysis Center (CDIAC) CO2 emissions in tonnes or metric tons (equivalent to approximately 2,204.6 pounds) per person by country NA
GDP per capita, percent yearly growth 1801 to 2019 Gapminder World Bank Growth Domestic Product (which is an overall measure of the health of nation’s economy) per person by country NA
Energy use per person 1960 to 2015 Gapminder World Bank Use of primary energy before transformation to other end-use fules, by country NA
Crude Mortality Rate 1960 to 2018 World Bank World Bank Death rate per 1,000 people by country NA
US Natural Disasters 1980 to 2019 The National Oceanic and Atmospheric Administration (NOAA) The National Oceanic and Atmospheric Administration (NOAA) US data about:
– Droughts
– Floods
– Freezes
– Severe Storms
– Tropical Cyclones
– Wildfires
– Winter Storms		 NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2020). https://www.ncdc.noaa.gov/billions/, DOI: 10.25921/stkw-7w73
Temperature 1895 to 2019 The National Oceanic and Atmospheric Administration (NOAA) The National Oceanic and Atmospheric Administration (NOAA) US National yearly average temperature (in Fahrenheit) from 1895 to 2019 NOAA National Centers for Environmental information, Climate at a Glance: National Time Series, published June 2020, retrieved on June 26, 2020 from https://www.ncdc.noaa.gov/cag/

To obtain the temperature data, annual average temperatures were selected as shown in this image:

Importantly, notice that the data we would like to use span different time periods:

Data Time span
CO2 emissions 1751 to 2014
GDP per capita, yearly growth 1801 to 2019
Energy use per person 1960 to 2015
Crude Mortality Rate 1960 to 2018
US Natural Disasters 1980 to 2019
Temperature 1895 to 2019

Data Import

To read in the files that were downloaded from the various sources as indicated in the table above, we will use the read_xlsx() and read_xls() functions of the readxl package to import the data from the .xlsx and .xls files respectively and we will use the read_csv function of the readr package to import the data from the csv files.

For our csv data files, there are some lines that we would like to not import - infact, we will get an error if we try to import them because our table structure will be as r expects. We can do so using the skip = argument of the read_csv() function.

Here you can see that the first two rows of the data about US Disasters doesn’t have the same number of columns as the subsequent rows. So we want to skip these first two lines, we will use skip = 2 for this.

Now looking at the temperature data, we can see that the first four lines do not have the same number of columns as the subsequent lines. We will skip importing all 4 lines by using skip = 4. We can also specify that NA values are encoded as "-99". This will replace all instances of "-99" with NA. We can do this using the na = argument of the read_csv() function. We will do so as: na = "-99". The “-99” needs to be in quotation markes becuase this argument expects characters.

Great! now we have imported all of the data that we will need.

Data Wrangling

Now we will take a look at our data and wrangle it until it is easy to use to allow us to evaluate how CO2 emissions have changed over time and how emissions may relate to energy use, mortality, GDP etc.

Yearly CO2 Emissions

First let’s take a look at the CO2 data. We can use the base head() function to see just the first 6 rows of our data.

We will use the %>% pipe which can be used to define the input for later sequential steps. This will make more sense when we have multiple sequential steps using the same data object. To use the pipe notation we need to install and load the dplyr package.

# A tibble: 6 x 265
  country `1751` `1752` `1753` `1754` `1755` `1756` `1757` `1758` `1759` `1760`
  <chr>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Afghan…     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
2 Albania     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
3 Algeria     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
4 Andorra     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
5 Angola      NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
6 Antigu…     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
# … with 254 more variables: `1761` <dbl>, `1762` <dbl>, `1763` <dbl>,
#   `1764` <dbl>, `1765` <dbl>, `1766` <dbl>, `1767` <dbl>, `1768` <dbl>,
#   `1769` <dbl>, `1770` <dbl>, `1771` <dbl>, `1772` <dbl>, `1773` <dbl>,
#   `1774` <dbl>, `1775` <dbl>, `1776` <dbl>, `1777` <dbl>, `1778` <dbl>,
#   `1779` <dbl>, `1780` <dbl>, `1781` <dbl>, `1782` <dbl>, `1783` <dbl>,
#   `1784` <dbl>, `1785` <dbl>, `1786` <dbl>, `1787` <dbl>, `1788` <dbl>,
#   `1789` <dbl>, `1790` <dbl>, `1791` <dbl>, `1792` <dbl>, `1793` <dbl>,
#   `1794` <dbl>, `1795` <dbl>, `1796` <dbl>, `1797` <dbl>, `1798` <dbl>,
#   `1799` <dbl>, `1800` <dbl>, `1801` <dbl>, `1802` <dbl>, `1803` <dbl>,
#   `1804` <dbl>, `1805` <dbl>, `1806` <dbl>, `1807` <dbl>, `1808` <dbl>,
#   `1809` <dbl>, `1810` <dbl>, `1811` <dbl>, `1812` <dbl>, `1813` <dbl>,
#   `1814` <dbl>, `1815` <dbl>, `1816` <dbl>, `1817` <dbl>, `1818` <dbl>,
#   `1819` <dbl>, `1820` <dbl>, `1821` <dbl>, `1822` <dbl>, `1823` <dbl>,
#   `1824` <dbl>, `1825` <dbl>, `1826` <dbl>, `1827` <dbl>, `1828` <dbl>,
#   `1829` <dbl>, `1830` <dbl>, `1831` <dbl>, `1832` <dbl>, `1833` <dbl>,
#   `1834` <dbl>, `1835` <dbl>, `1836` <dbl>, `1837` <dbl>, `1838` <dbl>,
#   `1839` <dbl>, `1840` <dbl>, `1841` <dbl>, `1842` <dbl>, `1843` <dbl>,
#   `1844` <dbl>, `1845` <dbl>, `1846` <dbl>, `1847` <dbl>, `1848` <dbl>,
#   `1849` <dbl>, `1850` <dbl>, `1851` <dbl>, `1852` <dbl>, `1853` <dbl>,
#   `1854` <dbl>, `1855` <dbl>, `1856` <dbl>, `1857` <dbl>, `1858` <dbl>,
#   `1859` <dbl>, `1860` <dbl>, …

OK, we can see that our country data makes of the rows and the yearly data makes up the columns. We also see that we have alot of NA values.

We can also use the glimpse() function of the dplyr packge to view our data. This allows us to see more of our data at once. We will see a tiny bit of each variable/column. To do so our data will be displayed with the column names listed on the right.

Rows: 192
Columns: 265
$ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "An…
$ `1751`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1752`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1753`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1754`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1755`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1756`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1757`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1758`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1759`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1760`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1761`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1762`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1763`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1764`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1765`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1766`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1767`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1768`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1769`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1770`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1771`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1772`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1773`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1774`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1775`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1776`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1777`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1778`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1779`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1780`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1781`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1782`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1783`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1784`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1785`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1786`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1787`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1788`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1789`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1790`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1791`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1792`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1793`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1794`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1795`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1796`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1797`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1798`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1799`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1800`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1801`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1802`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1803`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1804`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1805`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1806`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1807`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 169, NA, NA, NA, NA, NA, …
$ `1808`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1809`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1810`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1811`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1812`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1813`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1814`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1815`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1816`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1817`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1818`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ `1819`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 253, NA, NA, NA, NA, NA, …
$ `1820`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 334, NA, NA, NA, NA, NA, …
$ `1821`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 359, NA, NA, NA, NA, NA, …
$ `1822`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 367, NA, NA, NA, NA, NA, …
$ `1823`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 348, NA, NA, NA, NA, NA, …
$ `1824`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 400, NA, NA, NA, NA, NA, …
$ `1825`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 403, NA, NA, NA, NA, NA, …
$ `1826`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 458, NA, NA, NA, NA, NA, …
$ `1827`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 477, NA, NA, NA, NA, NA, …
$ `1828`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 458, NA, NA, NA, NA, NA, …
$ `1829`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 477, NA, NA, NA, NA, NA, …
$ `1830`  <dbl> NA, NA, NA, NA, NA, NA, NA, 0.032, NA, 495.000, 0.308, NA, NA…
$ `1831`  <dbl> NA, NA, NA, NA, NA, NA, NA, 3.84e-02, NA, 4.80e+02, 3.70e-01,…
$ `1832`  <dbl> NA, NA, NA, NA, NA, NA, NA, 2.56e-02, NA, 5.13e+02, 2.47e-01,…
$ `1833`  <dbl> NA, NA, NA, NA, NA, NA, NA, 0.032, NA, 429.000, 0.308, NA, NA…
$ `1834`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 587, NA, NA, NA, NA, NA, …
$ `1835`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 634, NA, NA, NA, NA, NA, …
$ `1836`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 675, NA, NA, NA, NA, NA, …
$ `1837`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 708, NA, NA, NA, NA, NA, …
$ `1838`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 851, NA, NA, NA, NA, NA, …
$ `1839`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1060, NA, NA, NA, NA, NA,…
$ `1840`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1170, NA, NA, NA, NA, NA,…
$ `1841`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1320, NA, NA, NA, NA, NA,…
$ `1842`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1460, NA, NA, NA, NA, NA,…
$ `1843`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1270, NA, NA, NA, NA, NA,…
$ `1844`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1600, NA, NA, NA, NA, NA,…
$ `1845`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 1800, NA, NA, NA, NA, NA,…
$ `1846`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2120, NA, NA, NA, NA, NA,…
$ `1847`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2080, NA, NA, NA, NA, NA,…
$ `1848`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, NA,…
$ `1849`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2260, NA, NA, NA, NA, NA,…
$ `1850`  <dbl> NA, NA, NA, NA, NA, NA, NA, 0.198, NA, 2330.000, 1.910, NA, N…
$ `1851`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2340, NA, NA, NA, NA, NA,…
$ `1852`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 2810, NA, NA, NA, NA, NA,…
$ `1853`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 3230, NA, NA, NA, NA, NA,…
$ `1854`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 3180, NA, NA, NA, NA, NA,…
$ `1855`  <dbl> NA, NA, NA, NA, NA, NA, NA, 6.01e-01, NA, 3.70e+03, 5.80e+00,…
$ `1856`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 4240, NA, NA, NA, NA, NA,…
$ `1857`  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 4880, NA, NA, NA, NA, NA,…
$ `1858`  <dbl> NA, NA, NA, NA, NA, NA, NA, 8.44e-01, NA, 7.25e+03, 8.14e+00,…
$ `1859`  <dbl> NA, NA, NA, NA, NA, NA, NA, 8.95e-01, NA, 5.87e+03, 8.64e+00,…
$ `1860`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.18, 279.00, 6150.00, 11.40, NA,…
$ `1861`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.5, 510.0, 6380.0, 14.5, NA, NA,…
$ `1862`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.36, 356.00, 6360.00, 13.10, NA,…
$ `1863`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.42, 400.00, 5880.00, 13.70, NA,…
$ `1864`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.59, 268.00, 5080.00, 15.40, NA,…
$ `1865`  <dbl> NA, NA, NA, NA, NA, NA, NA, 1.52, 422.00, 5360.00, 14.70, NA,…
$ `1866`  <dbl> NA, NA, NA, NA, NA, NA, NA, 4.81, 697.00, 3600.00, 46.40, NA,…
$ `1867`  <dbl> NA, NA, NA, NA, NA, NA, NA, 5.52, 895.00, 4920.00, 53.20, NA,…
$ `1868`  <dbl> NA, NA, NA, NA, NA, NA, NA, 4.59, 733.00, 6080.00, 44.30, NA,…
$ `1869`  <dbl> NA, NA, NA, NA, NA, NA, NA, 6.23, 642.00, 6490.00, 60.10, NA,…
$ `1870`  <dbl> NA, NA, NA, NA, NA, NA, NA, 6.76, 601.00, 7370.00, 65.20, NA,…
$ `1871`  <dbl> NA, NA, NA, NA, NA, NA, NA, 9.12, 693.00, 10200.00, 88.00, NA…
$ `1872`  <dbl> NA, NA, NA, NA, NA, NA, NA, 9.36, 708.00, 10000.00, 90.40, NA…
$ `1873`  <dbl> NA, NA, NA, NA, NA, NA, NA, 8.79, 869.00, 10700.00, 84.80, NA…
$ `1874`  <dbl> NA, NA, NA, NA, NA, NA, NA, 10.7, 891.0, 9160.0, 103.0, NA, N…
$ `1875`  <dbl> NA, NA, NA, NA, NA, NA, NA, 12.3, 829.0, 7870.0, 119.0, NA, N…
$ `1876`  <dbl> NA, NA, NA, NA, NA, NA, NA, 15.2, 931.0, 8100.0, 147.0, NA, N…
$ `1877`  <dbl> NA, NA, NA, NA, NA, NA, NA, 15.6, 1070.0, 7290.0, 150.0, NA, …
$ `1878`  <dbl> NA, NA, NA, NA, NA, NA, NA, 20.3, 968.0, 7250.0, 196.0, NA, N…
$ `1879`  <dbl> NA, NA, NA, NA, NA, NA, NA, 20.9, 1460.0, 8870.0, 201.0, NA, …
$ `1880`  <dbl> NA, NA, NA, NA, NA, NA, NA, 24.5, 2210.0, 23700.0, 236.0, NA,…
$ `1881`  <dbl> NA, NA, NA, NA, NA, NA, NA, 25.80, 1770.00, 10300.00, 249.00,…
$ `1882`  <dbl> NA, NA, NA, NA, NA, NA, NA, 27.20, 2010.00, 10600.00, 262.00,…
$ `1883`  <dbl> NA, NA, NA, NA, NA, NA, NA, 30.90, 2430.00, 11800.00, 298.00,…
$ `1884`  <dbl> NA, NA, NA, NA, NA, NA, NA, 31.4, 2570.0, 11500.0, 303.0, NA,…
$ `1885`  <dbl> NA, NA, NA, NA, NA, NA, NA, 34.20, 2910.00, 12100.00, 330.00,…
$ `1886`  <dbl> NA, NA, NA, NA, NA, NA, NA, 35.10, 2890.00, 11400.00, 338.00,…
$ `1887`  <dbl> NA, NA, NA, NA, NA, NA, 1090.0, 37.1, 3040.0, 12300.0, 358.0,…
$ `1888`  <dbl> NA, NA, NA, NA, NA, NA, 891.0, 38.7, 3530.0, 12000.0, 373.0, …
$ `1889`  <dbl> NA, NA, NA, NA, NA, NA, 1760.0, 41.8, 3430.0, 12900.0, 403.0,…
$ `1890`  <dbl> NA, NA, NA, NA, NA, NA, 1370.0, 47.3, 3550.0, 13000.0, 457.0,…
$ `1891`  <dbl> NA, NA, NA, NA, NA, NA, 939.0, 52.1, 4010.0, 15000.0, 503.0, …
$ `1892`  <dbl> NA, NA, NA, NA, NA, NA, 1390.0, 55.1, 4150.0, 14500.0, 532.0,…
$ `1893`  <dbl> NA, NA, NA, NA, NA, NA, 1550.0, 64.6, 3970.0, 17700.0, 624.0,…
$ `1894`  <dbl> NA, NA, NA, NA, NA, NA, 1990.0, 65.8, 4360.0, 18100.0, 635.0,…
$ `1895`  <dbl> NA, NA, NA, NA, NA, NA, 2270.0, 75.6, 4590.0, 20400.0, 730.0,…
$ `1896`  <dbl> NA, NA, NA, NA, NA, NA, 2310, 77, 4510, 21300, 743, NA, NA, N…
$ `1897`  <dbl> NA, NA, NA, NA, NA, NA, 2080, 89, 4980, 23000, 859, NA, NA, N…
$ `1898`  <dbl> NA, NA, NA, NA, NA, NA, 2350.0, 99.9, 5620.0, 24500.0, 964.0,…
$ `1899`  <dbl> NA, NA, NA, NA, NA, NA, 2920, 116, 5790, 24800, 1120, NA, NA,…
$ `1900`  <dbl> NA, NA, NA, NA, NA, NA, 2070, 131, 10200, 27700, 1270, NA, NA…
$ `1901`  <dbl> NA, NA, NA, NA, NA, NA, 2490, 135, 11400, 28400, 1300, NA, NA…
$ `1902`  <dbl> NA, NA, NA, NA, NA, NA, 2820, 130, 11400, 25700, 1260, NA, NA…
$ `1903`  <dbl> NA, NA, NA, NA, NA, NA, 2860, 127, 11200, 25600, 1230, NA, NA…
$ `1904`  <dbl> NA, NA, NA, NA, NA, NA, 3800, 142, 11600, 26900, 1370, NA, NA…
$ `1905`  <dbl> NA, NA, NA, NA, NA, NA, 3990, 126, 12100, 28100, 1220, NA, NA…
$ `1906`  <dbl> NA, NA, NA, NA, NA, NA, 6260, 144, 14400, 33600, 1390, NA, NA…
$ `1907`  <dbl> NA, NA, NA, NA, NA, NA, 6260, 161, 15500, 42200, 1560, NA, NA…
$ `1908`  <dbl> NA, NA, NA, NA, NA, NA, 7620, 162, 16800, 59000, 1570, NA, NA…
$ `1909`  <dbl> NA, NA, NA, NA, NA, NA, 5940, 172, 14600, 42200, 1660, NA, NA…
$ `1910`  <dbl> NA, NA, NA, NA, NA, NA, 8910, 168, 17500, 57600, 1620, NA, NA…
$ `1911`  <dbl> NA, NA, NA, NA, NA, NA, 9950, 174, 19300, 48100, 1680, NA, NA…
$ `1912`  <dbl> NA, NA, NA, NA, NA, NA, 9490, 198, 20800, 50000, 1910, NA, NA…
$ `1913`  <dbl> NA, NA, NA, NA, NA, NA, 10200, 215, 22400, 59700, 2070, NA, N…
$ `1914`  <dbl> NA, NA, NA, NA, NA, NA, 8680, 194, 24500, 48900, 1870, NA, NA…
$ `1915`  <dbl> NA, NA, NA, NA, NA, NA, 6950, 178, 21800, 34900, 1720, NA, NA…
$ `1916`  <dbl> NA, NA, 3.67, NA, NA, NA, 4990.00, 189.00, 19300.00, 8040.00,…
$ `1917`  <dbl> NA, NA, 7.33, NA, NA, NA, 2230.00, 174.00, 20800.00, 3450.00,…
$ `1918`  <dbl> NA, NA, 18.3, NA, NA, NA, 2520.0, 69.5, 23000.0, 3340.0, 671.…
$ `1919`  <dbl> NA, NA, 18.3, NA, NA, NA, 3730.0, 59.4, 21800.0, 3020.0, 573.…
$ `1920`  <dbl> NA, NA, 22.0, NA, NA, NA, 5900.0, 54.2, 25800.0, 14500.0, 523…
$ `1921`  <dbl> NA, NA, 25.7, NA, NA, NA, 5540.0, 58.7, 23200.0, 19400.0, 567…
$ `1922`  <dbl> NA, NA, 25.7, NA, NA, NA, 7300.0, 71.6, 24400.0, 18600.0, 692…
$ `1923`  <dbl> NA, NA, 14.7, NA, NA, NA, 8450.0, 79.1, 24900.0, 17800.0, 764…
$ `1924`  <dbl> NA, NA, 29.3, NA, NA, NA, 11000.0, 94.3, 27100.0, 20100.0, 91…
$ `1925`  <dbl> NA, NA, 33.0, NA, NA, NA, 11200.0, 93.1, 28300.0, 19000.0, 89…
$ `1926`  <dbl> NA, NA, 40.3, NA, NA, NA, 11300.0, 135.0, 27900.0, 18600.0, 1…
$ `1927`  <dbl> NA, NA, 58.7, NA, NA, NA, 13400.0, 168.0, 28900.0, 20100.0, 1…
$ `1928`  <dbl> NA, NA, 73.30, NA, NA, NA, 12800.00, 186.00, 26300.00, 21200.…
$ `1929`  <dbl> NA, NA, 80.70, NA, NA, NA, 13100.00, 201.00, 23700.00, 24200.…
$ `1930`  <dbl> NA, NA, 84.30, NA, NA, NA, 12800.00, 273.00, 22000.00, 18900.…
$ `1931`  <dbl> NA, NA, 99.00, NA, NA, NA, 12900.00, 328.00, 19600.00, 18100.…
$ `1932`  <dbl> NA, NA, 114.00, NA, NA, NA, 13100.00, 369.00, 20400.00, 15200…
$ `1933`  <dbl> NA, 7.33, 121.00, NA, NA, NA, 13200.00, 412.00, 21600.00, 142…
$ `1934`  <dbl> NA, 7.33, 139.00, NA, NA, NA, 14300.00, 499.00, 22700.00, 138…
$ `1935`  <dbl> NA, 18.3, 132.0, NA, NA, NA, 14000.0, 565.0, 25300.0, 13900.0…
$ `1936`  <dbl> NA, 128.0, 51.3, NA, NA, NA, 15100.0, 648.0, 27100.0, 13600.0…
$ `1937`  <dbl> NA, 297.0, 69.7, NA, NA, NA, 16700.0, 662.0, 28900.0, 15300.0…
$ `1938`  <dbl> NA, 348, 33, NA, NA, NA, 16400, 699, 28100, 5790, 6750, NA, 3…
$ `1939`  <dbl> NA, 433.00, 161.00, NA, NA, NA, 17400.00, 707.00, 32200.00, 6…
$ `1940`  <dbl> NA, 693, 238, NA, NA, NA, 15900, 848, 29100, 7350, 8190, NA, …
$ `1941`  <dbl> NA, 627, 312, NA, NA, NA, 14000, 745, 34600, 7980, 7190, NA, …
$ `1942`  <dbl> NA, 744, 499, NA, NA, NA, 13500, 513, 36500, 8560, 4950, NA, …
$ `1943`  <dbl> NA, 462, 469, NA, NA, NA, 14100, 655, 35000, 9620, 6320, NA, …
$ `1944`  <dbl> NA, 154, 499, NA, NA, NA, 14000, 613, 34200, 9400, 5920, NA, …
$ `1945`  <dbl> NA, 121, 616, NA, NA, NA, 13700, 649, 32700, 4570, 6270, NA, …
$ `1946`  <dbl> NA, 484, 763, NA, NA, NA, 13700, 730, 35500, 12800, 7040, NA,…
$ `1947`  <dbl> NA, 928.00, 744.00, NA, NA, NA, 14500.00, 878.00, 38000.00, 1…
$ `1948`  <dbl> NA, 704.00, 803.00, NA, NA, NA, 17400.00, 935.00, 38500.00, 2…
$ `1949`  <dbl> 14.70, 1020.00, 909.00, NA, NA, NA, 15400.00, 1060.00, 37700.…
$ `1950`  <dbl> 84.3, 297.0, 3790.0, NA, 187.0, NA, 30000.0, 1180.0, 54800.0,…
$ `1951`  <dbl> 91.7, 403.0, 4140.0, NA, 249.0, NA, 35000.0, 1280.0, 59100.0,…
$ `1952`  <dbl> 91.7, 374.0, 3890.0, NA, 312.0, NA, 36100.0, 1370.0, 60300.0,…
$ `1953`  <dbl> 106.0, 414.0, 4000.0, NA, 275.0, NA, 35200.0, 1450.0, 59500.0…
$ `1954`  <dbl> 106.0, 502.0, 4160.0, NA, 348.0, NA, 36800.0, 1590.0, 67900.0…
$ `1955`  <dbl> 154.0, 664.0, 4610.0, NA, 414.0, NA, 39600.0, 1800.0, 70700.0…
$ `1956`  <dbl> 183.0, 840.0, 5000.0, NA, 502.0, NA, 44300.0, 1970.0, 73100.0…
$ `1957`  <dbl> 293.0, 1510.0, 5540.0, NA, 620.0, 22.0, 47700.0, 2160.0, 7460…
$ `1958`  <dbl> 330.0, 1200.0, 5220.0, NA, 594.0, 29.3, 44200.0, 2310.0, 7770…
$ `1959`  <dbl> 385.0, 1440.0, 5670.0, NA, 620.0, 29.3, 49000.0, 2430.0, 8380…
$ `1960`  <dbl> 414.0, 2020.0, 6160.0, NA, 550.0, 36.7, 48800.0, 2530.0, 8820…
$ `1961`  <dbl> 491.0, 2280.0, 6070.0, NA, 455.0, 47.7, 51200.0, 2600.0, 9060…
$ `1962`  <dbl> 689.0, 2460.0, 5670.0, NA, 1180.0, 103.0, 53700.0, 2730.0, 94…
$ `1963`  <dbl> 708.0, 2080.0, 5430.0, NA, 1150.0, 84.3, 50100.0, 2930.0, 101…
$ `1964`  <dbl> 840.0, 2020.0, 5650.0, NA, 1220.0, 91.7, 55700.0, 3120.0, 109…
$ `1965`  <dbl> 1010.0, 2170.0, 6600.0, NA, 1190.0, 150.0, 58900.0, 3310.0, 1…
$ `1966`  <dbl> 1090.0, 2550.0, 8430.0, NA, 1550.0, 348.0, 63100.0, 3490.0, 1…
$ `1967`  <dbl> 1280, 2680, 8440, NA, 994, 565, 65500, 3650, 129000, 40000, 3…
$ `1968`  <dbl> 1220, 3070, 9060, NA, 1670, 990, 69100, 3750, 135000, 42400, …
$ `1969`  <dbl> 942, 3250, 11300, NA, 2790, 1260, 77300, 3910, 142000, 44700,…
$ `1970`  <dbl> 1.67e+03, 3.74e+03, 1.51e+04, NA, 3.58e+03, 4.62e+02, 8.27e+0…
$ `1971`  <dbl> 1.90e+03, 4.35e+03, 1.87e+04, NA, 3.41e+03, 4.25e+02, 8.89e+0…
$ `1972`  <dbl> 1.53e+03, 5.64e+03, 2.83e+04, NA, 4.51e+03, 3.74e+02, 9.02e+0…
$ `1973`  <dbl> 1.64e+03, 5.29e+03, 3.83e+04, NA, 4.88e+03, 3.30e+02, 9.41e+0…
$ `1974`  <dbl> 1.92e+03, 4.35e+03, 3.19e+04, NA, 4.87e+03, 4.29e+02, 9.56e+0…
$ `1975`  <dbl> 2.13e+03, 4.59e+03, 3.20e+04, NA, 4.42e+03, 7.08e+02, 9.49e+0…
$ `1976`  <dbl> 1.99e+03, 4.95e+03, 3.92e+04, NA, 3.29e+03, 4.03e+02, 9.98e+0…
$ `1977`  <dbl> 2.39e+03, 5.72e+03, 4.19e+04, NA, 3.53e+03, 4.66e+02, 1.01e+0…
$ `1978`  <dbl> 2160, 6490, 62500, NA, 5410, 491, 103000, 5810, 202000, 57500…
$ `1979`  <dbl> 2240, 7590, 45600, NA, 5500, 407, 111000, 5850, 205000, 61600…
$ `1980`  <dbl> 1760, 5170, 66500, NA, 5350, 143, 109000, 6080, 221000, 52300…
$ `1981`  <dbl> 1980.0, 7340.0, 46400.0, NA, 5280.0, 106.0, 102000.0, 5970.0,…
$ `1982`  <dbl> 2100, 7310, 39300, NA, 4650, 293, 103000, 6080, 234000, 53900…
$ `1983`  <dbl> 2520.0, 7630.0, 52600.0, NA, 5120.0, 84.3, 105000.0, 6170.0, …
$ `1984`  <dbl> 2830.0, 7830.0, 71100.0, NA, 5010.0, 147.0, 107000.0, 6230.0,…
$ `1985`  <dbl> 3510.0, 7880.0, 72800.0, NA, 4700.0, 249.0, 101000.0, 6710.0,…
$ `1986`  <dbl> 3140, 8060, 76300, NA, 4660, 249, 104000, 6730, 240000, 54100…
$ `1987`  <dbl> 3120, 7440, 84100, NA, 5820, 275, 115000, 7020, 256000, 57700…
$ `1988`  <dbl> 2870, 7330, 83900, NA, 5130, 286, 121000, 7210, 261000, 53300…
$ `1989`  <dbl> 2780.0, 8980.0, 80000.0, NA, 5010.0, 286.0, 117000.0, 7060.0,…
$ `1990`  <dbl> 2610, 5520, 77000, 407, 5120, 282, 112000, 6620, 264000, 5770…
$ `1991`  <dbl> 2440, 4290, 79000, 407, 5090, 268, 117000, 6380, 261000, 6160…
$ `1992`  <dbl> 1390, 2520, 80100, 407, 5200, 264, 121000, 5830, 268000, 5670…
$ `1993`  <dbl> 1350, 2340, 82200, 411, 5780, 271, 118000, 2560, 277000, 5710…
$ `1994`  <dbl> 1290, 1930, 86400, 407, 3890, 268, 122000, 2710, 278000, 5710…
$ `1995`  <dbl> 1240, 2090, 95300, 425, 11000, 275, 128000, 3410, 282000, 598…
$ `1996`  <dbl> 1180, 2020, 97100, 455, 10500, 293, 135000, 2560, 302000, 632…
$ `1997`  <dbl> 1100, 1540, 87300, 466, 7380, 308, 138000, 3230, 306000, 6270…
$ `1998`  <dbl> 1040, 1750, 107000, 491, 7310, 319, 140000, 3360, 317000, 637…
$ `1999`  <dbl> 821, 2980, 92000, 513, 9160, 330, 147000, 3010, 325000, 61900…
$ `2000`  <dbl> 774, 3020, 87900, 524, 9540, 345, 142000, 3470, 329000, 62300…
$ `2001`  <dbl> 818, 3220, 84200, 524, 9730, 348, 134000, 3540, 325000, 65900…
$ `2002`  <dbl> 1070, 3750, 89900, 532, 12700, 370, 125000, 3040, 341000, 671…
$ `2003`  <dbl> 1200, 4290, 91600, 535, 9060, 403, 135000, 3430, 336000, 7220…
$ `2004`  <dbl> 950, 4170, 88500, 561, 18800, 422, 158000, 3640, 343000, 7240…
$ `2005`  <dbl> 1330, 4250, 107000, 576, 19200, 429, 162000, 4350, 350000, 74…
$ `2006`  <dbl> 1650, 3900, 101000, 546, 22300, 444, 175000, 4380, 365000, 72…
$ `2007`  <dbl> 2270, 3930, 109000, 539, 25200, 469, 175000, 5060, 372000, 69…
$ `2008`  <dbl> 4210, 4370, 110000, 539, 25700, 480, 189000, 5560, 386000, 69…
$ `2009`  <dbl> 6770, 4380, 121000, 517, 27800, 510, 180000, 4360, 395000, 62…
$ `2010`  <dbl> 8460, 4600, 119000, 517, 29100, 524, 188000, 4220, 391000, 67…
$ `2011`  <dbl> 12200, 5240, 121000, 491, 30300, 513, 192000, 4920, 392000, 6…
$ `2012`  <dbl> 10800, 4910, 130000, 488, 33400, 524, 192000, 5690, 388000, 6…
$ `2013`  <dbl> 10000, 5060, 134000, 477, 32600, 524, 190000, 5500, 372000, 6…
$ `2014`  <dbl> 9810, 5720, 145000, 462, 34800, 532, 204000, 5530, 361000, 58…

Indicator n 1 CO2 Emissions (Mg) 57246 2 Deaths/1000 People 57246 3 Disasters 40 4 Energy Use (kg, oil-eq./capita) 57246 5 GDP Growth/Capita (%) 57246 6 Temperature (Fahrenheit) 116

We can see that we have a large tibble. A tibble is the tidyverse version of a data frame. It is essentially a table with variable information arranged as columns, and individual observations arranged as rows. We can see that the tibble gives us information about the class of each variable. For example the country variable is made up of character (abbreviated as chr) values. We see that we have 265 different country variables and CO2 emission values for 192 different years (from 1751 to 2014). Recall that the values are emissions in metric tons also called tonnes. We can see that there are fewer NA values for later years.

Now we will modify this data to make it more usable for making visualizations. One thing we will use is the %<>% opperator which is from the magrittr package. This allows us to use our CO2_emissions data and reassign it to a modified version at the same time.

We will use the pivot_longer() function of the dplyr package to convert our data into what is called long format. This means that we will have more rows and fewer columns than our current format. This is done by collapsing multiple variables into fewer variables.

We want to collapse all of the values for the emission data across the different individual year variables into one new emission variable and we will identify what year they are from using a new Year variable.

# A tibble: 6 x 3
  country     Year  Emissions
  <chr>       <chr>     <dbl>
1 Afghanistan 1751         NA
2 Afghanistan 1752         NA
3 Afghanistan 1753         NA
4 Afghanistan 1754         NA
5 Afghanistan 1755         NA
6 Afghanistan 1756         NA

We also want to rename the country variable to be capitalized. W We can use the rename() function of the dplyr package to rename this variable. When renaming variables the new name is listed first before the =. We will also modify the Emissions data by dividing it by 1000 to make the numbers smaller. To do this we will use the mutate() function, which is also part of the dplyr() package. This function allows us to create and modify variables. You may also note that the Year variable is currently of class type character. We would like to change it to be numeric. This can also be accomplished using the mutate() function.

Now let’s take a look to see how our data has changed:

# A tibble: 6 x 4
  Country   Year Emissions Label                      
  <chr>    <dbl>     <dbl> <chr>                      
1 Zimbabwe  2009      5600 CO2 Emissions (Metric Tons)
2 Zimbabwe  2010      7780 CO2 Emissions (Metric Tons)
3 Zimbabwe  2011      9560 CO2 Emissions (Metric Tons)
4 Zimbabwe  2012      7790 CO2 Emissions (Metric Tons)
5 Zimbabwe  2013     11700 CO2 Emissions (Metric Tons)
6 Zimbabwe  2014     12000 CO2 Emissions (Metric Tons)

Great, we can see that now the Year variable is of class double (abbreviated dbl), which is a numeric class.

Yearly Growth in GDP per Capita

# A tibble: 6 x 220
  country   `1801`   `1802`   `1803`   `1804`   `1805`   `1806`   `1807`
  <chr>      <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>    <dbl>
1 Afghan… NA       NA       NA       NA       NA       NA       NA      
2 Albania  0.104    0.104    0.104    0.104    0.104    0.104    0.104  
3 Algeria -0.00247 -0.00247 -0.00247 -0.00247 -0.00247 -0.00247 -0.00247
4 Andorra  0.166    0.166    0.166    0.166    0.166    0.166    0.166  
5 Angola   0.425    0.425    0.425    0.425    0.425    0.425    0.425  
6 Antigu… NA       NA       NA       NA       NA       NA       NA      
# … with 212 more variables: `1808` <dbl>, `1809` <dbl>, `1810` <dbl>,
#   `1811` <dbl>, `1812` <dbl>, `1813` <dbl>, `1814` <dbl>, `1815` <dbl>,
#   `1816` <dbl>, `1817` <dbl>, `1818` <dbl>, `1819` <dbl>, `1820` <dbl>,
#   `1821` <dbl>, `1822` <dbl>, `1823` <dbl>, `1824` <dbl>, `1825` <dbl>,
#   `1826` <dbl>, `1827` <dbl>, `1828` <dbl>, `1829` <dbl>, `1830` <dbl>,
#   `1831` <dbl>, `1832` <dbl>, `1833` <dbl>, `1834` <dbl>, `1835` <dbl>,
#   `1836` <dbl>, `1837` <dbl>, `1838` <dbl>, `1839` <dbl>, `1840` <dbl>,
#   `1841` <dbl>, `1842` <dbl>, `1843` <dbl>, `1844` <dbl>, `1845` <dbl>,
#   `1846` <dbl>, `1847` <dbl>, `1848` <dbl>, `1849` <dbl>, `1850` <dbl>,
#   `1851` <dbl>, `1852` <dbl>, `1853` <dbl>, `1854` <dbl>, `1855` <dbl>,
#   `1856` <dbl>, `1857` <dbl>, `1858` <dbl>, `1859` <dbl>, `1860` <dbl>,
#   `1861` <dbl>, `1862` <dbl>, `1863` <dbl>, `1864` <dbl>, `1865` <dbl>,
#   `1866` <dbl>, `1867` <dbl>, `1868` <dbl>, `1869` <dbl>, `1870` <dbl>,
#   `1871` <dbl>, `1872` <dbl>, `1873` <dbl>, `1874` <dbl>, `1875` <dbl>,
#   `1876` <dbl>, `1877` <dbl>, `1878` <dbl>, `1879` <dbl>, `1880` <dbl>,
#   `1881` <dbl>, `1882` <dbl>, `1883` <dbl>, `1884` <dbl>, `1885` <dbl>,
#   `1886` <dbl>, `1887` <dbl>, `1888` <dbl>, `1889` <dbl>, `1890` <dbl>,
#   `1891` <dbl>, `1892` <dbl>, `1893` <dbl>, `1894` <dbl>, `1895` <dbl>,
#   `1896` <dbl>, `1897` <dbl>, `1898` <dbl>, `1899` <dbl>, `1900` <dbl>,
#   `1901` <dbl>, `1902` <dbl>, `1903` <dbl>, `1904` <dbl>, `1905` <dbl>,
#   `1906` <dbl>, `1907` <dbl>, …
  [1] "country" "1801"    "1802"    "1803"    "1804"    "1805"    "1806"   
  [8] "1807"    "1808"    "1809"    "1810"    "1811"    "1812"    "1813"   
 [15] "1814"    "1815"    "1816"    "1817"    "1818"    "1819"    "1820"   
 [22] "1821"    "1822"    "1823"    "1824"    "1825"    "1826"    "1827"   
 [29] "1828"    "1829"    "1830"    "1831"    "1832"    "1833"    "1834"   
 [36] "1835"    "1836"    "1837"    "1838"    "1839"    "1840"    "1841"   
 [43] "1842"    "1843"    "1844"    "1845"    "1846"    "1847"    "1848"   
 [50] "1849"    "1850"    "1851"    "1852"    "1853"    "1854"    "1855"   
 [57] "1856"    "1857"    "1858"    "1859"    "1860"    "1861"    "1862"   
 [64] "1863"    "1864"    "1865"    "1866"    "1867"    "1868"    "1869"   
 [71] "1870"    "1871"    "1872"    "1873"    "1874"    "1875"    "1876"   
 [78] "1877"    "1878"    "1879"    "1880"    "1881"    "1882"    "1883"   
 [85] "1884"    "1885"    "1886"    "1887"    "1888"    "1889"    "1890"   
 [92] "1891"    "1892"    "1893"    "1894"    "1895"    "1896"    "1897"   
 [99] "1898"    "1899"    "1900"    "1901"    "1902"    "1903"    "1904"   
[106] "1905"    "1906"    "1907"    "1908"    "1909"    "1910"    "1911"   
[113] "1912"    "1913"    "1914"    "1915"    "1916"    "1917"    "1918"   
[120] "1919"    "1920"    "1921"    "1922"    "1923"    "1924"    "1925"   
[127] "1926"    "1927"    "1928"    "1929"    "1930"    "1931"    "1932"   
[134] "1933"    "1934"    "1935"    "1936"    "1937"    "1938"    "1939"   
[141] "1940"    "1941"    "1942"    "1943"    "1944"    "1945"    "1946"   
[148] "1947"    "1948"    "1949"    "1950"    "1951"    "1952"    "1953"   
[155] "1954"    "1955"    "1956"    "1957"    "1958"    "1959"    "1960"   
[162] "1961"    "1962"    "1963"    "1964"    "1965"    "1966"    "1967"   
[169] "1968"    "1969"    "1970"    "1971"    "1972"    "1973"    "1974"   
[176] "1975"    "1976"    "1977"    "1978"    "1979"    "1980"    "1981"   
[183] "1982"    "1983"    "1984"    "1985"    "1986"    "1987"    "1988"   
[190] "1989"    "1990"    "1991"    "1992"    "1993"    "1994"    "1995"   
[197] "1996"    "1997"    "1998"    "1999"    "2000"    "2001"    "2002"   
[204] "2003"    "2004"    "2005"    "2006"    "2007"    "2008"    "2009"   
[211] "2010"    "2011"    "2012"    "2013"    "2014"    "2015"    "2016"   
[218] "2017"    "2018"    "2019"   

Rows: 194
Columns: 220
$ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "An…
$ `1801`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1802`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1803`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1804`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1805`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1806`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1807`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1808`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1809`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1810`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1811`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1812`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1813`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1814`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1815`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1816`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1817`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1818`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1819`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1820`  <dbl> NA, 0.10400, -0.00247, 0.16600, 0.42500, NA, NA, NA, 0.21600,…
$ `1821`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1822`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1823`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1824`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1825`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1826`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1827`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1828`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1829`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1830`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1831`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1832`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1833`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1834`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1835`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1836`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1837`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1838`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1839`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1840`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1841`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1842`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1843`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1844`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1845`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1846`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1847`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1848`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1849`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1850`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1851`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1852`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1853`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1854`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1855`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1856`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1857`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1858`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1859`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1860`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1861`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1862`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1863`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1864`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1865`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1866`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1867`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1868`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1869`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1870`  <dbl> 0.32500, 0.21300, 1.02000, 1.17000, 0.42500, 0.66100, 1.41000…
$ `1871`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 0.772…
$ `1872`  <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 1.4100, 0.371…
$ `1873`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 7.600…
$ `1874`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 0.292…
$ `1875`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 1.410, 0.371, 7.910…
$ `1876`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -0.952, 0.371, -3.1…
$ `1877`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 7.210, 0.371, 0.720…
$ `1878`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -8.110, 0.371, 5.98…
$ `1879`  <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 1.2700, 0.371…
$ `1880`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -5.020, 0.371, 1.91…
$ `1881`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -1.500, 0.371, 3.95…
$ `1882`  <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 22.8000, 0.37…
$ `1883`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 9.000, 0.371, 10.20…
$ `1884`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 3.980, 0.371, -3.84…
$ `1885`  <dbl> 0.3250, 1.4700, 1.1400, 1.1700, 0.4250, 0.6610, 14.2000, 0.37…
$ `1886`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -2.700, -4.080, -2.…
$ `1887`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 3.690, 16.700, 6.98…
$ `1888`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 12.900, -4.020, -2.…
$ `1889`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, 6.590, -7.220, 5.40…
$ `1890`  <dbl> 0.325, 1.470, 1.140, 1.170, 0.425, 0.661, -11.300, -0.635, -6…
$ `1891`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, -8.460, -8.610, 4.6…
$ `1892`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 16.300, 9.230, -14.…
$ `1893`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 2.840, 13.100, -7.2…
$ `1894`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 12.200, 13.400, 1.5…
$ `1895`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 7.760, -7.670, -7.3…
$ `1896`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 7.520, 9.890, 5.670…
$ `1897`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, -21.900, -1.890, -7…
$ `1898`  <dbl> 0.3250, 1.3700, 1.1400, 1.1700, 0.4250, 0.6610, 5.4600, 2.430…
$ `1899`  <dbl> 0.325, 1.370, 1.140, 1.170, 0.425, 0.661, 14.700, 5.880, -1.3…
$ `1900`  <dbl> 0.3250, 1.3700, 1.1400, 1.1700, 0.4250, 0.6610, -14.8000, -2.…
$ `1901`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 5.620, 2.270, -4.35…
$ `1902`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, -4.850, 8.420, -0.4…
$ `1903`  <dbl> 0.3250, 1.3100, 1.1400, 1.1700, 0.4250, 0.6610, 11.5000, -7.1…
$ `1904`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 7.830, 10.300, 5.36…
$ `1905`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 10.400, -11.700, -0…
$ `1906`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 0.392, -4.640, 5.26…
$ `1907`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, -2.530, -4.130, 2.4…
$ `1908`  <dbl> 0.3250, 1.3100, 1.1400, 1.1700, 0.4250, 0.6610, 5.1600, 8.960…
$ `1909`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 0.294, 3.590, 6.130…
$ `1910`  <dbl> 0.325, 1.310, 1.140, 1.170, 0.425, 0.661, 2.640, 6.460, 4.600…
$ `1911`  <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, -2.2000…
$ `1912`  <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, 4.17000…
$ `1913`  <dbl> 0.32500, 1.28000, 1.14000, 1.17000, 0.42500, 0.66100, -2.9600…
$ `1914`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -14.300, -4.720, -2…
$ `1915`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -3.470, 2.690, -2.5…
$ `1916`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -4.610, -3.150, -0.…
$ `1917`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -9.830, -16.700, -1…
$ `1918`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 16.600, -16.700, -3…
$ `1919`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 1.950, -16.700, 2.4…
$ `1920`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 5.550, -5.070, 1.06…
$ `1921`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -0.488, -5.070, 3.0…
$ `1922`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.950, 8.450, 3.110…
$ `1923`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 7.970, 8.450, 2.540…
$ `1924`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.750, 8.450, 4.340…
$ `1925`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, -3.460, 8.450, 2.50…
$ `1926`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 2.080, 8.450, 0.351…
$ `1927`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 4.350, 8.450, -0.51…
$ `1928`  <dbl> 0.46300, 0.83700, 0.43200, 3.80000, 2.96000, 2.45000, 3.45000…
$ `1929`  <dbl> 0.463, 0.837, 0.432, 3.800, 2.960, 2.450, 1.860, 8.450, -3.46…
$ `1930`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -6.880, 4.280, -10.…
$ `1931`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -8.810, 0.680, -7.5…
$ `1932`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -5.180, -1.760, 4.8…
$ `1933`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.830, 3.530, 6.110…
$ `1934`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 6.020, 8.930, 4.490…
$ `1935`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.480, 14.100, 5.09…
$ `1936`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -0.737, 6.560, 3.73…
$ `1937`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 5.680, 8.050, 4.170…
$ `1938`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -1.260, -0.505, 2.4…
$ `1939`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 2.260, 3.820, -0.69…
$ `1940`  <dbl> 0.4630, 0.3720, 0.4320, 3.8000, 2.9600, 2.4500, 0.0522, -4.38…
$ `1941`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 3.670, -2.100, 10.1…
$ `1942`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -0.457, -2.100, 10.…
$ `1943`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -2.240, -2.100, 2.6…
$ `1944`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 9.710, -2.100, -4.4…
$ `1945`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -4.770, -2.100, -6.…
$ `1946`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 6.610, -2.100, -4.6…
$ `1947`  <dbl> 0.4630, 0.3720, 0.4320, 3.8000, 2.9600, 2.4500, 8.8000, 10.90…
$ `1948`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, 3.160, 12.700, 4.55…
$ `1949`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -3.620, 8.980, 3.87…
$ `1950`  <dbl> 0.463, 0.372, 0.432, 3.800, 2.960, 2.450, -1.110, 8.070, 2.42…
$ `1951`  <dbl> 1.250, 4.320, -1.300, 3.800, 2.320, 2.450, 1.750, -1.470, 1.2…
$ `1952`  <dbl> 1.660, 0.160, 2.150, 3.800, 2.320, 2.450, -7.090, 4.430, -1.1…
$ `1953`  <dbl> 4.290, 4.040, -0.517, 3.800, 2.220, 2.450, 3.320, 2.360, 1.18…
$ `1954`  <dbl> 0.3080, 2.9400, 4.9900, 3.8000, -4.1100, 2.4500, 2.2100, 2.87…
$ `1955`  <dbl> 0.129, 5.390, 0.573, 3.800, 6.410, 2.450, 5.200, 6.420, 3.040…
$ `1956`  <dbl> 2.530, 1.010, 7.460, 3.800, -3.350, 2.450, 0.962, 7.410, 1.01…
$ `1957`  <dbl> -1.9400, 6.4100, 9.0300, 3.8000, 7.8600, 2.4500, 3.4200, 0.04…
$ `1958`  <dbl> 3.520, 4.500, 1.510, 3.800, 3.620, 2.450, 4.390, 5.390, 2.660…
$ `1959`  <dbl> 0.764, 4.120, 16.000, 3.800, -1.230, 2.450, -8.140, -3.080, 3…
$ `1960`  <dbl> 1.430, 5.060, 4.750, 3.800, 2.260, 2.450, 6.220, 7.290, 1.880…
$ `1961`  <dbl> -1.280, 0.831, -13.800, 3.800, 11.400, 2.450, 5.480, 3.870, -…
$ `1962`  <dbl> -0.497, 3.310, -20.400, 3.800, -4.360, 2.450, -3.180, 1.030, …
$ `1963`  <dbl> -0.429, 3.410, 23.400, 3.800, 3.380, 2.450, -3.930, -3.740, 4…
$ `1964`  <dbl> -0.374, 3.420, 2.160, 3.800, 9.360, 2.450, 8.760, 11.400, 4.7…
$ `1965`  <dbl> -0.124, 3.650, 3.530, 3.800, 5.710, 2.450, 7.640, 4.390, 3.08…
$ `1966`  <dbl> -1.370, 3.750, -7.730, 3.800, 4.040, 2.450, -0.829, 3.670, 0.…
$ `1967`  <dbl> 0.310, 3.760, 5.740, 3.800, 4.040, 2.450, 1.220, 3.310, 4.800…
$ `1968`  <dbl> 1.06, 3.61, 8.40, 3.80, -3.19, 2.45, 2.87, 4.81, 3.87, 3.90, …
$ `1969`  <dbl> -0.883, 3.400, 6.480, 3.800, 1.150, 2.450, 7.080, 0.450, 3.70…
$ `1970`  <dbl> -0.514, 3.690, 6.830, 3.800, 4.510, 2.450, 3.850, 6.690, 4.00…
$ `1971`  <dbl> -7.160, 4.000, -11.100, -0.603, 3.880, 4.800, 2.130, 1.650, 2…
$ `1972`  <dbl> -4.360, 3.920, 17.500, 2.760, -1.970, 4.720, 0.370, -0.430, 0…
$ `1973`  <dbl> 8.580, 4.980, 0.285, 2.630, 5.680, 6.090, 2.020, 7.380, 3.820…
$ `1974`  <dbl> 2.750, 0.373, 3.010, 0.870, 0.714, 1.700, 3.700, 1.070, 0.831…
$ `1975`  <dbl> 2.5300, 0.3480, 3.8900, -3.5900, -7.3700, -6.2900, -2.2400, -…
$ `1976`  <dbl> 2.330, 0.400, 3.420, -0.531, -7.630, -8.910, -1.580, 2.850, 2…
$ `1977`  <dbl> -9.2400, 0.4380, 5.7700, -0.6340, -1.8700, 8.2100, 4.8600, 0.…
$ `1978`  <dbl> 5.210, 0.460, 9.420, -1.920, -7.870, 4.990, -4.730, 0.760, 1.…
$ `1979`  <dbl> -2.180, 0.487, 5.750, -3.670, -2.600, 12.600, 5.440, -2.140, …
$ `1980`  <dbl> 0.1680, 0.7060, -1.2600, -2.1000, -0.4710, 8.3500, 0.0163, -1…
$ `1981`  <dbl> 10.700, 0.536, -0.656, -4.970, -7.610, 6.330, -6.960, -0.756,…
$ `1982`  <dbl> 9.0500, 0.5500, 3.0800, -4.0500, -3.5200, 1.5500, -4.5000, 0.…
$ `1983`  <dbl> 3.59000, 0.58400, 1.89000, -3.47000, 0.52000, 8.03000, 2.6700…
$ `1984`  <dbl> -1.830, 0.569, 2.270, -2.810, 2.440, 8.850, 0.681, -0.516, 5.…
$ `1985`  <dbl> -3.280, 0.523, 2.020, -1.370, 0.316, 9.540, -8.340, -0.847, 3…
$ `1986`  <dbl> 7.170, 0.635, -3.790, 0.612, 0.332, 10.700, 5.880, 2.330, 0.6…
$ `1987`  <dbl> -17.3000, 0.6290, -3.3100, 3.7300, 4.3100, 11.3000, 1.2500, -…
$ `1988`  <dbl> -9.660, 0.633, -4.670, 3.540, 3.020, 10.000, -3.220, 0.453, 2…
$ `1989`  <dbl> -2.410, 0.754, 0.771, 2.780, -2.140, 7.540, -8.340, 0.135, 2.…
$ `1990`  <dbl> -5.5800, 0.8930, -3.9100, 0.8110, -3.1700, 3.2300, -3.2200, -…
$ `1991`  <dbl> -0.572, -28.900, -3.490, -1.470, -2.030, 1.540, 9.290, -13.30…
$ `1992`  <dbl> -7.950, -8.100, -0.752, -3.740, -8.830, -0.632, 8.540, -40.80…
$ `1993`  <dbl> -13.900, 8.780, -4.440, -5.650, -26.400, 3.080, 4.660, -4.660…
$ `1994`  <dbl> -10.400, 7.440, -3.070, -1.650, -1.860, 3.660, 4.770, 8.960, …
$ `1995`  <dbl> 20.300, 12.600, 1.710, -0.114, 11.600, -6.630, -3.910, 8.900,…
$ `1996`  <dbl> 2.660, 8.650, 1.940, 3.090, 16.600, 3.900, 4.460, 6.080, 2.48…
$ `1997`  <dbl> 2.8200, -10.6000, -0.5580, 8.5900, 2.7000, 2.3400, 7.0500, 3.…
$ `1998`  <dbl> 2.8300, 12.3000, 3.5000, 3.3000, -2.6300, 1.8200, 2.7900, 7.6…
$ `1999`  <dbl> 2.7100, 9.5800, 1.6800, 4.0100, 0.3870, 1.6900, -4.4500, 3.35…
$ `2000`  <dbl> -1.0500, 6.7900, 0.9950, 0.4010, -0.0561, -0.6830, -1.8500, 6…
$ `2001`  <dbl> -10.400, 6.690, 1.130, 10.000, -0.171, 0.251, -5.470, 10.100,…
$ `2002`  <dbl> 22.1000, 2.8600, 2.5500, 3.5800, 10.7000, 0.8110, -12.0000, 1…
$ `2003`  <dbl> 8.040, 5.450, 5.460, 4.170, -0.247, 3.670, 7.770, 14.400, 3.0…
$ `2004`  <dbl> 2.5000, 5.3600, 3.8400, 4.1800, 7.4500, 5.7500, 7.9200, 10.90…
$ `2005`  <dbl> 8.6100, 4.9600, 3.8000, 4.2100, 16.6000, 3.2900, 8.1200, 14.3…
$ `2006`  <dbl> 1.590, 5.270, 0.188, 2.370, 15.000, 11.300, 7.250, 13.100, 1.…
$ `2007`  <dbl> 10.800, 5.410, 1.850, -1.700, 19.600, 5.780, 7.440, 13.600, 2…
$ `2008`  <dbl> 0.117, 6.840, 0.472, -5.600, 10.600, 0.378, 5.570, 6.690, 0.7…
$ `2009`  <dbl> 17.300, 2.910, 0.179, -6.310, -0.464, -11.700, -0.276, -15.00…
$ `2010`  <dbl> 5.1700, 2.9800, 2.0600, -4.7800, 0.5940, -8.5300, 7.9400, 1.1…
$ `2011`  <dbl> 3.8500, 2.4900, 0.8570, -4.3000, 1.0300, -2.9600, 7.6500, 3.6…
$ `2012`  <dbl> 11.200, 2.280, 1.160, NA, 2.130, 2.790, 0.761, 6.920, 1.780, …
$ `2013`  <dbl> 1.130, 1.720, 1.610, NA, 1.030, 0.468, 3.090, 2.980, 1.170, 0…
$ `2014`  <dbl> 0.837, 2.610, 2.180, NA, 2.240, 1.620, -0.622, 4.050, 1.410, …
$ `2015`  <dbl> 2.110, 3.820, 2.100, NA, 2.460, 1.900, -0.128, 4.290, 1.480, …
$ `2016`  <dbl> 2.680, 4.720, 2.360, NA, 2.770, 2.200, 0.367, 4.490, 1.730, 1…
$ `2017`  <dbl> 2.760, 5.030, 2.500, NA, 0.262, 2.200, 0.861, 4.790, 1.700, 1…
$ `2018`  <dbl> 3.020, 5.030, 2.630, NA, 3.460, 2.200, 0.861, 4.790, 1.710, 1…
$ `2019`  <dbl> 3.380, 5.230, 2.680, NA, 3.550, 2.200, 0.861, 4.790, 1.770, 0…

Again, we will use the pivot_longer() to transform the data to long format. We will also again change the country variable to be Country by using the rename() function , and we will make the Year varaible numeric using the mutate() function.

AVOCADO… this was in Michael’s code… but I don’t know that we need it We will use the drop_na() function of the tidyr package to drop all years with missing data.

Now let’s see how this data has changed:

# A tibble: 6 x 4
  Country      Year   GDP Label                
  <chr>       <dbl> <dbl> <chr>                
1 Afghanistan  1801    NA GDP Growth/Capita (%)
2 Afghanistan  1802    NA GDP Growth/Capita (%)
3 Afghanistan  1803    NA GDP Growth/Capita (%)
4 Afghanistan  1804    NA GDP Growth/Capita (%)
5 Afghanistan  1805    NA GDP Growth/Capita (%)
6 Afghanistan  1806    NA GDP Growth/Capita (%)
# A tibble: 219 x 2
    Year     n
   <dbl> <int>
 1  1801   194
 2  1802   194
 3  1803   194
 4  1804   194
 5  1805   194
 6  1806   194
 7  1807   194
 8  1808   194
 9  1809   194
10  1810   194
# … with 209 more rows

Energy Use per Person

Now let’s take a look at the energy use per person data:

# A tibble: 6 x 57
  country `1960` `1961` `1962` `1963` `1964` `1965` `1966` `1967` `1968` `1969`
  <chr>    <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Albania     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
2 Algeria     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
3 Angola      NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
4 Antigu…     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
5 Argent…     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
6 Armenia     NA     NA     NA     NA     NA     NA     NA     NA     NA     NA
# … with 46 more variables: `1970` <dbl>, `1971` <dbl>, `1972` <dbl>,
#   `1973` <dbl>, `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>,
#   `1978` <dbl>, `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>,
#   `1983` <dbl>, `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>,
#   `1988` <dbl>, `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>,
#   `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>,
#   `1998` <dbl>, `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, `2002` <dbl>,
#   `2003` <dbl>, `2004` <dbl>, `2005` <dbl>, `2006` <dbl>, `2007` <dbl>,
#   `2008` <dbl>, `2009` <dbl>, `2010` <dbl>, `2011` <dbl>, `2012` <dbl>,
#   `2013` <dbl>, `2014` <dbl>, `2015` <dbl>

Rows: 169
Columns: 57
$ country <chr> "Albania", "Algeria", "Angola", "Antigua and Barbuda", "Argen…
$ `1960`  <dbl> NA, NA, NA, NA, NA, NA, 3060, 1550, NA, NA, NA, NA, NA, NA, 2…
$ `1961`  <dbl> NA, NA, NA, NA, NA, NA, 3120, 1550, NA, NA, NA, NA, NA, NA, 2…
$ `1962`  <dbl> NA, NA, NA, NA, NA, NA, 3170, 1680, NA, NA, NA, NA, NA, NA, 2…
$ `1963`  <dbl> NA, NA, NA, NA, NA, NA, 3280, 1820, NA, NA, NA, NA, NA, NA, 3…
$ `1964`  <dbl> NA, NA, NA, NA, NA, NA, 3350, 1860, NA, NA, NA, NA, NA, NA, 3…
$ `1965`  <dbl> NA, NA, NA, NA, NA, NA, 3460, 1850, NA, NA, NA, NA, NA, NA, 3…
$ `1966`  <dbl> NA, NA, NA, NA, NA, NA, 3550, 1900, NA, NA, NA, NA, NA, NA, 3…
$ `1967`  <dbl> NA, NA, NA, NA, NA, NA, 3690, 1920, NA, NA, NA, NA, NA, NA, 3…
$ `1968`  <dbl> NA, NA, NA, NA, NA, NA, 3760, 2050, NA, NA, NA, NA, NA, NA, 3…
$ `1969`  <dbl> NA, NA, NA, NA, NA, NA, 3790, 2180, NA, NA, NA, NA, NA, NA, 3…
$ `1970`  <dbl> NA, NA, NA, NA, NA, NA, 4060, 2420, NA, NA, NA, NA, NA, NA, 4…
$ `1971`  <dbl> 785.0, 232.0, 556.0, NA, 1380.0, NA, 3990.0, 2510.0, NA, NA, …
$ `1972`  <dbl> 866.0, 261.0, 584.0, NA, 1380.0, NA, 4040.0, 2630.0, NA, NA, …
$ `1973`  <dbl> 763.0, 305.0, 568.0, NA, 1410.0, NA, 4260.0, 2830.0, NA, NA, …
$ `1974`  <dbl> 777.0, 319.0, 565.0, NA, 1420.0, NA, 4290.0, 2730.0, NA, NA, …
$ `1975`  <dbl> 827.0, 330.0, 536.0, NA, 1380.0, NA, 4350.0, 2650.0, NA, NA, …
$ `1976`  <dbl> 891, 367, 515, NA, 1400, NA, 4410, 2870, NA, NA, 9580, 98, NA…
$ `1977`  <dbl> 924.0, 399.0, 494.0, NA, 1420.0, NA, 4670.0, 2800.0, NA, NA, …
$ `1978`  <dbl> 1010.0, 477.0, 527.0, NA, 1430.0, NA, 4630.0, 2890.0, NA, NA,…
$ `1979`  <dbl> 864.0, 586.0, 518.0, NA, 1480.0, NA, 4680.0, 3140.0, NA, NA, …
$ `1980`  <dbl> 1150, 579, 511, NA, 1490, NA, 4740, 3070, NA, NA, 7790, 103, …
$ `1981`  <dbl> 989, 611, 497, NA, 1430, NA, 4690, 2900, NA, NA, 8300, 102, N…
$ `1982`  <dbl> 967, 771, 473, NA, 1420, NA, 4820, 2830, NA, NA, 9070, 105, N…
$ `1983`  <dbl> 1000, 808, 469, NA, 1420, NA, 4560, 2840, NA, NA, 8500, 105, …
$ `1984`  <dbl> 1020, 776, 458, NA, 1450, NA, 4650, 2950, NA, NA, 8830, 104, …
$ `1985`  <dbl> 917, 786, 470, NA, 1360, NA, 4600, 3050, NA, NA, 9920, 107, N…
$ `1986`  <dbl> 964, 862, 462, NA, 1420, NA, 4620, 3060, NA, NA, 10300, 111, …
$ `1987`  <dbl> 922, 828, 461, NA, 1480, NA, 4770, 3170, NA, NA, 9520, 107, N…
$ `1988`  <dbl> 928, 850, 467, NA, 1500, NA, 4700, 3200, NA, NA, 10500, 114, …
$ `1989`  <dbl> 896, 820, 465, NA, 1440, NA, 5000, 3140, NA, NA, 10200, 117, …
$ `1990`  <dbl> 813, 856, 483, 1480, 1410, 2180, 5060, 3240, 3170, 2520, 1060…
$ `1991`  <dbl> 573, 884, 480, NA, 1430, 2320, 4930, 3420, 3090, NA, 10100, 1…
$ `1992`  <dbl> 418, 884, 467, NA, 1480, 1200, 4960, 3250, 2460, NA, 10800, 1…
$ `1993`  <dbl> 412, 868, 468, NA, 1470, 652, 5150, 3260, 2180, NA, 11100, 12…
$ `1994`  <dbl> 441, 819, 459, NA, 1540, 420, 5090, 3230, 1950, NA, 11600, 12…
$ `1995`  <dbl> 417, 839, 445, NA, 1540, 511, 5130, 3370, 1810, NA, 11400, 13…
$ `1996`  <dbl> 448, 798, 445, NA, 1580, 562, 5390, 3580, 1510, NA, 11100, 13…
$ `1997`  <dbl> 385, 805, 443, NA, 1610, 594, 5470, 3550, 1440, NA, 12200, 13…
$ `1998`  <dbl> 427, 821, 430, NA, 1650, 610, 5550, 3610, 1490, NA, 12400, 13…
$ `1999`  <dbl> 576, 864, 439, NA, 1660, 594, 5610, 3590, 1370, NA, 11900, 13…
$ `2000`  <dbl> 580, 866, 437, NA, 1660, 656, 5640, 3570, 1400, NA, 12000, 13…
$ `2001`  <dbl> 597, 856, 442, NA, 1560, 657, 5450, 3760, 1410, NA, 11700, 14…
$ `2002`  <dbl> 660, 904, 447, NA, 1500, 618, 5570, 3770, 1410, NA, 11500, 15…
$ `2003`  <dbl> 648, 949, 466, NA, 1590, 657, 5570, 3970, 1480, NA, 11600, 15…
$ `2004`  <dbl> 715, 948, 462, 1530, 1720, 698, 5600, 4010, 1540, 2060, 10900…
$ `2005`  <dbl> 720, 974, 431, 1530, 1710, 843, 5560, 4090, 1600, 2110, 11700…
$ `2006`  <dbl> 707, 1030, 456, 1580, 1840, 865, 5710, 4080, 1560, 2100, 1160…
$ `2007`  <dbl> 680, 1070, 470, 1600, 1850, 973, 5870, 4020, 1410, 2070, 1120…
$ `2008`  <dbl> 711, 1070, 491, NA, 1920, 1030, 5960, 4030, 1520, NA, 11300, …
$ `2009`  <dbl> 732, 1150, 514, NA, 1850, 904, 5860, 3800, 1330, NA, 10300, 1…
$ `2010`  <dbl> 729, 1110, 521, NA, 1910, 863, 5790, 4050, 1280, NA, 10200, 2…
$ `2011`  <dbl> 765, 1140, 522, NA, 1930, 944, 5750, 3920, 1370, NA, 9910, 20…
$ `2012`  <dbl> 688, 1220, 553, NA, 1920, 1030, 5570, 3890, 1470, NA, 9660, 2…
$ `2013`  <dbl> 801, 1240, 534, NA, 1950, 1000, 5460, 3920, 1470, NA, 10400, …
$ `2014`  <dbl> 808, 1320, 545, NA, 2020, 1020, 5330, 3760, 1500, NA, 10600, …
$ `2015`  <dbl> NA, NA, NA, NA, NA, NA, 5480, 3800, NA, NA, NA, NA, NA, NA, 4…

To wrangle the energy_use data, we will again convert the data to long format, rename some variables, and mutate the Year data to be numeric.

# A tibble: 6 x 4
  Country   Year Energy Label                          
  <chr>    <dbl>  <dbl> <chr>                          
1 Zimbabwe  2010    671 Energy Use (kg, oil-eq./capita)
2 Zimbabwe  2011    713 Energy Use (kg, oil-eq./capita)
3 Zimbabwe  2012    735 Energy Use (kg, oil-eq./capita)
4 Zimbabwe  2013    750 Energy Use (kg, oil-eq./capita)
5 Zimbabwe  2014     NA Energy Use (kg, oil-eq./capita)
6 Zimbabwe  2015     NA Energy Use (kg, oil-eq./capita)

Crude Mortality Rate

# A tibble: 6 x 64
  `Data Source` `World Developm… ...3  ...4  ...5  ...6  ...7  ...8  ...9  ...10
  <chr>         <chr>            <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 Last Updated… 43819            <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
2 <NA>          <NA>             <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA>  <NA> 
3 Country Name  Country Code     Indi… Indi… 1960  1961  1962  1963  1964  1965 
4 Aruba         ABW              Deat… SP.D… 6.38… 6.24… 6.11… 6.01… 5.91… 5.83…
5 Afghanistan   AFG              Deat… SP.D… 32.2… 31.6… 31.0… 30.5… 30.0… 29.5…
6 Angola        AGO              Deat… SP.D… 27.0… 26.8… 26.6… 26.4… 26.1… 25.9…
# … with 54 more variables: ...11 <chr>, ...12 <chr>, ...13 <chr>, ...14 <chr>,
#   ...15 <chr>, ...16 <chr>, ...17 <chr>, ...18 <chr>, ...19 <chr>,
#   ...20 <chr>, ...21 <chr>, ...22 <chr>, ...23 <chr>, ...24 <chr>,
#   ...25 <chr>, ...26 <chr>, ...27 <chr>, ...28 <chr>, ...29 <chr>,
#   ...30 <chr>, ...31 <chr>, ...32 <chr>, ...33 <chr>, ...34 <chr>,
#   ...35 <chr>, ...36 <chr>, ...37 <chr>, ...38 <chr>, ...39 <chr>,
#   ...40 <chr>, ...41 <chr>, ...42 <chr>, ...43 <chr>, ...44 <chr>,
#   ...45 <chr>, ...46 <chr>, ...47 <chr>, ...48 <chr>, ...49 <chr>,
#   ...50 <chr>, ...51 <chr>, ...52 <chr>, ...53 <chr>, ...54 <chr>,
#   ...55 <chr>, ...56 <chr>, ...57 <chr>, ...58 <chr>, ...59 <chr>,
#   ...60 <chr>, ...61 <chr>, ...62 <chr>, ...63 <chr>, ...64 <chr>

We can see that there are a couple of empty rows which indicate when the data was updated. We can also see that the columns really start at the 3rd row. So first we will repace the column names with the 3rd row. Then we will remove the first 3 rows.

 [1] "Data Source"                  "World Development Indicators"
 [3] "...3"                         "...4"                        
 [5] "...5"                         "...6"                        
 [7] "...7"                         "...8"                        
 [9] "...9"                         "...10"                       
[11] "...11"                        "...12"                       
[13] "...13"                        "...14"                       
[15] "...15"                        "...16"                       
[17] "...17"                        "...18"                       
[19] "...19"                        "...20"                       
[21] "...21"                        "...22"                       
[23] "...23"                        "...24"                       
[25] "...25"                        "...26"                       
[27] "...27"                        "...28"                       
[29] "...29"                        "...30"                       
[31] "...31"                        "...32"                       
[33] "...33"                        "...34"                       
[35] "...35"                        "...36"                       
[37] "...37"                        "...38"                       
[39] "...39"                        "...40"                       
[41] "...41"                        "...42"                       
[43] "...43"                        "...44"                       
[45] "...45"                        "...46"                       
[47] "...47"                        "...48"                       
[49] "...49"                        "...50"                       
[51] "...51"                        "...52"                       
[53] "...53"                        "...54"                       
[55] "...55"                        "...56"                       
[57] "...57"                        "...58"                       
[59] "...59"                        "...60"                       
[61] "...61"                        "...62"                       
[63] "...63"                        "...64"                       
 [1] "Country Name"   "Country Code"   "Indicator Name" "Indicator Code"
 [5] "1960"           "1961"           "1962"           "1963"          
 [9] "1964"           "1965"           "1966"           "1967"          
[13] "1968"           "1969"           "1970"           "1971"          
[17] "1972"           "1973"           "1974"           "1975"          
[21] "1976"           "1977"           "1978"           "1979"          
[25] "1980"           "1981"           "1982"           "1983"          
[29] "1984"           "1985"           "1986"           "1987"          
[33] "1988"           "1989"           "1990"           "1991"          
[37] "1992"           "1993"           "1994"           "1995"          
[41] "1996"           "1997"           "1998"           "1999"          
[45] "2000"           "2001"           "2002"           "2003"          
[49] "2004"           "2005"           "2006"           "2007"          
[53] "2008"           "2009"           "2010"           "2011"          
[57] "2012"           "2013"           "2014"           "2015"          
[61] "2016"           "2017"           "2018"           "2019"          

Rows: 264
Columns: 64
$ `Country Name`   <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorr…
$ `Country Code`   <chr> "ABW", "AFG", "AGO", "ALB", "AND", "ARB", "ARE", "AR…
$ `Indicator Name` <chr> "Death rate, crude (per 1,000 people)", "Death rate,…
$ `Indicator Code` <chr> "SP.DYN.CDRT.IN", "SP.DYN.CDRT.IN", "SP.DYN.CDRT.IN"…
$ `1960`           <chr> "6.3879999999999999", "32.219000000000001", "27.0970…
$ `1961`           <chr> "6.2409999999999997", "31.649000000000001", "26.8590…
$ `1962`           <chr> "6.1180000000000003", "31.093", "26.626999999999999"…
$ `1963`           <chr> "6.0119999999999996", "30.550999999999998", "26.407"…
$ `1964`           <chr> "5.9199999999999999", "30.021999999999998", "26.1939…
$ `1965`           <chr> "5.8390000000000004", "29.501000000000001", "25.9660…
$ `1966`           <chr> "5.7699999999999996", "28.984999999999999", "25.6900…
$ `1967`           <chr> "5.7160000000000002", "28.468", "25.341999999999999"…
$ `1968`           <chr> "5.6820000000000004", "27.946000000000002", "24.916"…
$ `1969`           <chr> "5.6660000000000004", "27.417999999999999", "24.4179…
$ `1970`           <chr> "5.6710000000000003", "26.879999999999999", "23.872"…
$ `1971`           <chr> "5.6980000000000004", "26.334", "23.312000000000001"…
$ `1972`           <chr> "5.7460000000000004", "25.780999999999999", "22.7770…
$ `1973`           <chr> "5.8120000000000003", "25.222000000000001", "22.2959…
$ `1974`           <chr> "5.8929999999999998", "24.658000000000001", "21.8850…
$ `1975`           <chr> "5.9809999999999999", "24.087", "21.547999999999998"…
$ `1976`           <chr> "6.0700000000000003", "23.507999999999999", "21.276"…
$ `1977`           <chr> "6.157", "22.920000000000002", "21.047000000000001",…
$ `1978`           <chr> "6.2359999999999998", "22.324000000000002", "20.8389…
$ `1979`           <chr> "6.3079999999999998", "21.719999999999999", "20.6469…
$ `1980`           <chr> "6.3760000000000003", "21.109000000000002", "20.4669…
$ `1981`           <chr> "6.444", "20.489999999999998", "20.297999999999998",…
$ `1982`           <chr> "6.5190000000000001", "19.864999999999998", "20.145"…
$ `1983`           <chr> "6.6020000000000003", "19.239999999999998", "20.009"…
$ `1984`           <chr> "6.6929999999999996", "18.617999999999999", "19.8889…
$ `1985`           <chr> "6.7850000000000001", "18.004999999999999", "19.7890…
$ `1986`           <chr> "6.8730000000000002", "17.405999999999999", "19.7100…
$ `1987`           <chr> "6.9480000000000004", "16.826000000000001", "19.651"…
$ `1988`           <chr> "7.0049999999999999", "16.268000000000001", "19.6099…
$ `1989`           <chr> "7.0430000000000001", "15.738", "19.579000000000001"…
$ `1990`           <chr> "7.0590000000000002", "15.241", "19.555", "5.9850000…
$ `1991`           <chr> "7.0540000000000003", "14.782999999999999", "19.5330…
$ `1992`           <chr> "7.0339999999999998", "14.362", "19.506", "6.1550000…
$ `1993`           <chr> "7.0049999999999999", "13.974", "19.463999999999999"…
$ `1994`           <chr> "6.9729999999999999", "13.616", "19.396000000000001"…
$ `1995`           <chr> "6.9429999999999996", "13.282", "19.292000000000002"…
$ `1996`           <chr> "6.9219999999999997", "12.964", "19.146000000000001"…
$ `1997`           <chr> "6.9109999999999996", "12.654999999999999", "18.9520…
$ `1998`           <chr> "6.915", "12.348000000000001", "18.706", "6.06700000…
$ `1999`           <chr> "6.9340000000000002", "12.037000000000001", "18.404"…
$ `2000`           <chr> "6.9710000000000001", "11.718", "18.036000000000001"…
$ `2001`           <chr> "7.0220000000000002", "11.387", "17.597000000000001"…
$ `2002`           <chr> "7.0839999999999996", "11.048", "17.09", "5.891", NA…
$ `2003`           <chr> "7.1539999999999999", "10.704000000000001", "16.5219…
$ `2004`           <chr> "7.2329999999999997", "10.356", "15.903", "6.0609999…
$ `2005`           <chr> "7.3200000000000003", "10.003", "15.24", "6.20600000…
$ `2006`           <chr> "7.4180000000000001", "9.6449999999999996", "14.539"…
$ `2007`           <chr> "7.5270000000000001", "9.2870000000000008", "13.815"…
$ `2008`           <chr> "7.6479999999999997", "8.9320000000000004", "13.0850…
$ `2009`           <chr> "7.7800000000000002", "8.5839999999999996", "12.3670…
$ `2010`           <chr> "7.9180000000000001", "8.25", "11.68", "6.8410000000…
$ `2011`           <chr> "8.0609999999999999", "7.9359999999999999", "11.039"…
$ `2012`           <chr> "8.2050000000000001", "7.6449999999999996", "10.4510…
$ `2013`           <chr> "8.3469999999999995", "7.3799999999999999", "9.92099…
$ `2014`           <chr> "8.4879999999999995", "7.141", "9.4540000000000006",…
$ `2015`           <chr> "8.6270000000000007", "6.9290000000000003", "9.05199…
$ `2016`           <chr> "8.7650000000000006", "6.742", "8.7159999999999993",…
$ `2017`           <chr> "8.907", "6.5750000000000002", "8.4320000000000004",…
$ `2018`           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ `2019`           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

That is looking better! However, we also want to remove some variables like: Country Code, Indicator Name, and Indicator Code. We can do that using the select() functio of the dplyr package. We can use the minus sign - to indicate what variables we dont want to keep. Otherwise, we will perform similar modifications as we performed on the other datasets.

# A tibble: 6 x 4
  Country  Year Deaths Label             
  <chr>   <dbl>  <dbl> <chr>             
1 Aruba    1960   6.39 Deaths/1000 People
2 Aruba    1961   6.24 Deaths/1000 People
3 Aruba    1962   6.12 Deaths/1000 People
4 Aruba    1963   6.01 Deaths/1000 People
5 Aruba    1964   5.92 Deaths/1000 People
6 Aruba    1965   5.84 Deaths/1000 People

US-specific Data

Now we will take a look at the US data about disasters and temperature.

Disasters

# A tibble: 6 x 57
   Year `Drought Count` `Drought Cost` `Drought Lower … `Drought Upper …
  <dbl>           <dbl>          <dbl>            <dbl>            <dbl>
1  1980               1           33.2             26.4             39.6
2  1981               0            0                0                0  
3  1982               0            0                0                0  
4  1983               1            7.8              5.5              9  
5  1984               0            0                0                0  
6  1985               0            0                0                0  
# … with 52 more variables: `Drought Lower 90` <dbl>, `Drought Upper 90` <dbl>,
#   `Drought Lower 95` <dbl>, `Drought Upper 95` <dbl>, `Flooding Count` <dbl>,
#   `Flooding Cost` <dbl>, `Flooding Lower 75` <dbl>, `Flooding Upper
#   75` <dbl>, `Flooding Lower 90` <dbl>, `Flooding Upper 90` <dbl>, `Flooding
#   Lower 95` <dbl>, `Flooding Upper 95` <dbl>, `Freeze Count` <dbl>, `Freeze
#   Cost` <dbl>, `Freeze Lower 75` <dbl>, `Freeze Upper 75` <dbl>, `Freeze
#   Lower 90` <dbl>, `Freeze Upper 90` <dbl>, `Freeze Lower 95` <dbl>, `Freeze
#   Upper 95` <dbl>, `Severe Storm Count` <dbl>, `Severe Storm Cost` <dbl>,
#   `Severe Storm Lower 75` <dbl>, `Severe Storm Upper 75` <dbl>, `Severe Storm
#   Lower 90` <dbl>, `Severe Storm Upper 90` <dbl>, `Severe Storm Lower
#   95` <dbl>, `Severe Storm Upper 95` <dbl>, `Tropical Cyclone Count` <dbl>,
#   `Tropical Cyclone Cost` <dbl>, `Tropical Cyclone Lower 75` <dbl>, `Tropical
#   Cyclone Upper 75` <dbl>, `Tropical Cyclone Lower 90` <dbl>, `Tropical
#   Cyclone Upper 90` <dbl>, `Tropical Cyclone Lower 95` <dbl>, `Tropical
#   Cyclone Upper 95` <dbl>, `Wildfire Count` <dbl>, `Wildfire Cost` <dbl>,
#   `Wildfire Lower 75` <dbl>, `Wildfire Upper 75` <dbl>, `Wildfire Lower
#   90` <dbl>, `Wildfire Upper 90` <dbl>, `Wildfire Lower 95` <dbl>, `Wildfire
#   Upper 95` <dbl>, `Winter Storm Count` <dbl>, `Winter Storm Cost` <dbl>,
#   `Winter Storm Lower 75` <dbl>, `Winter Storm Upper 75` <dbl>, `Winter Storm
#   Lower 90` <dbl>, `Winter Storm Upper 90` <dbl>, `Winter Storm Lower
#   95` <dbl>, `Winter Storm Upper 95` <dbl>

We are specifically interested in the Year and the variables that contain the word "Count" so we will select them using the select() and contains() functions in the dplyr package. Since we are selecting for variables with the word "Count" we need to use quotation marks around it. Selecting for the variable year does not require this as that is actually the name of one of the existing variables.

# A tibble: 6 x 8
   Year `Drought Count` `Flooding Count` `Freeze Count` `Severe Storm C…
  <dbl>           <dbl>            <dbl>          <dbl>            <dbl>
1  1980               1                1              0                0
2  1981               0                0              1                1
3  1982               0                0              0                2
4  1983               1                2              1                0
5  1984               0                0              0                2
6  1985               0                0              1                0
# … with 3 more variables: `Tropical Cyclone Count` <dbl>, `Wildfire
#   Count` <dbl>, `Winter Storm Count` <dbl>

Now we want to create a new variable that will be the sum of all the different types of disasters for each year.

We can create this ne variable using the mutate() function of dplyr and we will use the base rowSums() function to perform the calculation. We dont want to include the Year variable in our sum, so we can exclude it using the selectfunction within the rowSums() function. However, to do so we need to indicate that we are using the data that we already used as input to our mutate() and rowSums() functions. We can do so by using a ..

Rows: 40
Columns: 9
$ Year                     <dbl> 1980, 1981, 1982, 1983, 1984, 1985, 1986, 19…
$ `Drought Count`          <dbl> 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,…
$ `Flooding Count`         <dbl> 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,…
$ `Freeze Count`           <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0,…
$ `Severe Storm Count`     <dbl> 0, 1, 2, 0, 2, 0, 1, 0, 0, 1, 1, 1, 4, 1, 1,…
$ `Tropical Cyclone Count` <dbl> 1, 0, 0, 1, 0, 3, 0, 0, 0, 1, 0, 1, 2, 0, 1,…
$ `Wildfire Count`         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,…
$ `Winter Storm Count`     <dbl> 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 2,…
$ Disasters                <dbl> 3, 2, 3, 5, 2, 5, 2, 0, 1, 5, 3, 4, 7, 5, 6,…

Great, now we are going to remove some of these variables and just keep or select using the select() function the variables we are interested in. We will keep the Flooding Count becuase as you may recall from earlier in this case study, events of extreme perciptation levels appear to be associated with global warming. We will use this as a proxy for that.

AVOCADO why is US listed in 3 variables? We are also going to add a new variable called Country to indicate that this data is from the United States. This will create a new variable where every value is United States. We will also create a new variable called Region where every value is US-specific and a new variable called Type where every value is United States.

# A tibble: 6 x 7
   Year Country       Indicator Value Region        Type       Label            
  <dbl> <chr>         <chr>     <dbl> <chr>         <chr>      <chr>            
1  1980 United States Disasters     3 United States US-specif… Number of Disast…
2  1981 United States Disasters     2 United States US-specif… Number of Disast…
3  1982 United States Disasters     3 United States US-specif… Number of Disast…
4  1983 United States Disasters     5 United States US-specif… Number of Disast…
5  1984 United States Disasters     2 United States US-specif… Number of Disast…
6  1985 United States Disasters     5 United States US-specif… Number of Disast…

Temperature

# A tibble: 6 x 3
    Date Value Anomaly
   <dbl> <dbl>   <dbl>
1 189512  50.3   -1.68
2 189612  52.0   -0.03
3 189712  51.6   -0.46
4 189812  51.4   -0.59
5 189912  51.0   -1.01
6 190012  52.8    0.75

OK, so we want to remove the Anomaly variable which is an indicator of how different the national average temperature for that year was from the average temperature from 1901-2000 which was 52.02°F.

We also want to change the date values, which are currently listed as the year followed by the number 12. To do so we want to just keep the first 4 characters in the Date variable string values. We can use the str_sub() function of the stringr package to do this. We just need to indicate the start and stop characters. In this case the start would be 1 and the 4th character would be where we want to stop, so we would use start = 1, stop = 4. Again we will create a Country, Region and Type variable. We will also change the name of the Date variable to Year so that it will be consistent with our other datasets. Furthermore, we also what it to be numeric. We can accomplish both renaming and changing to numeric by using the mutate() function. We canthen remove the Date variable and also order the columns just like the other us data using the select() function.

# A tibble: 6 x 7
   Year Country      Indicator   Value Region      Type      Label              
  <dbl> <chr>        <chr>       <dbl> <chr>       <chr>     <chr>              
1  1895 United Stat… Temperature  50.3 United Sta… US-speci… Temperature (Fahre…
2  1896 United Stat… Temperature  52.0 United Sta… US-speci… Temperature (Fahre…
3  1897 United Stat… Temperature  51.6 United Sta… US-speci… Temperature (Fahre…
4  1898 United Stat… Temperature  51.4 United Sta… US-speci… Temperature (Fahre…
5  1899 United Stat… Temperature  51.0 United Sta… US-speci… Temperature (Fahre…
6  1900 United Stat… Temperature  52.8 United Sta… US-speci… Temperature (Fahre…

Joining data

Now we would like to join the different datasets together into one tibble. To do so it is often necessary to have at least one column or variable with the same name to be used as a key for putting your data together. To put all of our data together there are several *_join() functions available in the dplyr package.

We will use the full_join() function as we have different time spans for each dataset and we would like to retain as much data as possible. Thefull_join() function will simply create NA values for any of the years that are not in one of the data sets. We can check by using the base summary() function. This will also allow us to check that there are column names that are consistent in each dataset that we wish to combine.

   Country               Year        Emissions           Label          
 Length:50688       Min.   :1751   Min.   :       0   Length:50688      
 Class :character   1st Qu.:1817   1st Qu.:     550   Class :character  
 Mode  :character   Median :1882   Median :    4390   Mode  :character  
                    Mean   :1882   Mean   :   83808                     
                    3rd Qu.:1948   3rd Qu.:   31925                     
                    Max.   :2014   Max.   :10300000                     
                                   NA's   :33772                        
   Country               Year           GDP             Label          
 Length:42486       Min.   :1801   Min.   :-67.500   Length:42486      
 Class :character   1st Qu.:1855   1st Qu.:  0.133   Class :character  
 Mode  :character   Median :1910   Median :  0.633   Mode  :character  
                    Mean   :1910   Mean   :  1.302                     
                    3rd Qu.:1965   3rd Qu.:  2.160                     
                    Max.   :2019   Max.   :145.000                     
                                   NA's   :2392                        
   Country               Year          Energy            Label          
 Length:9464        Min.   :1960   Min.   :    9.58   Length:9464       
 Class :character   1st Qu.:1974   1st Qu.:  505.75   Class :character  
 Mode  :character   Median :1988   Median : 1185.00   Mode  :character  
                    Mean   :1988   Mean   : 2238.82                     
                    3rd Qu.:2001   3rd Qu.: 3030.00                     
                    Max.   :2015   Max.   :22000.00                     
                                   NA's   :3544                         
   Country               Year          Deaths          Label          
 Length:15840       Min.   :1960   Min.   : 1.127   Length:15840      
 Class :character   1st Qu.:1975   1st Qu.: 7.083   Class :character  
 Mode  :character   Median :1990   Median : 9.375   Mode  :character  
                    Mean   :1990   Mean   :10.673                     
                    3rd Qu.:2004   3rd Qu.:12.722                     
                    Max.   :2019   Max.   :54.444                     
                                   NA's   :1603

Indeed, Country, and Year variables are present in all of the datasets. We can see that the minimum and maximum year is different for nearly all the datasets.

We need to specify what columns/variables we will be joining by using the by = argument in the full_join() function.

# A tibble: 6 x 7
  Country      Year Emissions Label                         GDP Energy Deaths
  <chr>       <dbl>     <dbl> <chr>                       <dbl>  <dbl>  <dbl>
1 Afghanistan  1751        NA CO2 Emissions (Metric Tons)    NA     NA     NA
2 Afghanistan  1752        NA CO2 Emissions (Metric Tons)    NA     NA     NA
3 Afghanistan  1753        NA CO2 Emissions (Metric Tons)    NA     NA     NA
4 Afghanistan  1754        NA CO2 Emissions (Metric Tons)    NA     NA     NA
5 Afghanistan  1755        NA CO2 Emissions (Metric Tons)    NA     NA     NA
6 Afghanistan  1756        NA CO2 Emissions (Metric Tons)    NA     NA     NA

We can also do the same thing using by using thereduce() function of the purrr package. This is a good option if you have many dasasets to combine.

# A tibble: 6 x 7
  Country      Year Emissions Label                         GDP Energy Deaths
  <chr>       <dbl>     <dbl> <chr>                       <dbl>  <dbl>  <dbl>
1 Afghanistan  1751        NA CO2 Emissions (Metric Tons)    NA     NA     NA
2 Afghanistan  1752        NA CO2 Emissions (Metric Tons)    NA     NA     NA
3 Afghanistan  1753        NA CO2 Emissions (Metric Tons)    NA     NA     NA
4 Afghanistan  1754        NA CO2 Emissions (Metric Tons)    NA     NA     NA
5 Afghanistan  1755        NA CO2 Emissions (Metric Tons)    NA     NA     NA
6 Afghanistan  1756        NA CO2 Emissions (Metric Tons)    NA     NA     NA
Rows: 118,478
Columns: 7
$ Country   <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan",…
$ Year      <dbl> 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760,…
$ Emissions <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Label     <chr> "CO2 Emissions (Metric Tons)", "CO2 Emissions (Metric Tons)…
$ GDP       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Energy    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ Deaths    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

Nice, looks good!

We will also make a long version of this data, where we will create an new variable called Indicator that will indicate what dataset the data came from and we will collapse the values from the columns called Emissions (CO2 Emissions (Mg)), GDP(GDP Growth/Capita (%)), Energy(Energy Use (kg, oil-eq./capita)), and Deaths (Deaths/1000 People).

# A tibble: 6 x 5
  Country      Year Label                       Indicator Value
  <chr>       <dbl> <chr>                       <chr>     <dbl>
1 Afghanistan  1751 CO2 Emissions (Metric Tons) Emissions    NA
2 Afghanistan  1751 CO2 Emissions (Metric Tons) GDP          NA
3 Afghanistan  1751 CO2 Emissions (Metric Tons) Energy       NA
4 Afghanistan  1751 CO2 Emissions (Metric Tons) Deaths       NA
5 Afghanistan  1752 CO2 Emissions (Metric Tons) Emissions    NA
6 Afghanistan  1752 CO2 Emissions (Metric Tons) GDP          NA

We will also create a new variable called Region that will indicate if the data is about the United States or a different country based on the values in the Country variable. We will use the case_when() function of the dplyr package to do this. If the Country variable is equal to "United States" the value for the new variable will also be “United States”, where as if the Country variable is not equal to "United States" but is some other character string value, such as "Afghanistan", then the value for the new variable will be "Rest of the World". The new values for the new variable Region are indicated after the specific conditional statements by using the ~ symbol. We will also create a new variable called Type, where all the values are "Global" to indicate that this data is not specific to just the United States.

# A tibble: 6 x 7
  Country      Year Label                   Indicator Value Region         Type 
  <chr>       <dbl> <chr>                   <chr>     <dbl> <chr>          <chr>
1 Afghanistan  1751 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
2 Afghanistan  1751 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…
3 Afghanistan  1751 CO2 Emissions (Metric … Energy       NA Rest of the W… Glob…
4 Afghanistan  1751 CO2 Emissions (Metric … Deaths       NA Rest of the W… Glob…
5 Afghanistan  1752 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
6 Afghanistan  1752 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…

We will now combine this data with the US data about disasters and temperatures.

We will now use the bind_rows() function which will just append the us_temperature data and the us_disaster data after the df_long data.

# A tibble: 6 x 7
   Year Country       Indicator Value Region        Type       Label            
  <dbl> <chr>         <chr>     <dbl> <chr>         <chr>      <chr>            
1  1980 United States Disasters     3 United States US-specif… Number of Disast…
2  1981 United States Disasters     2 United States US-specif… Number of Disast…
3  1982 United States Disasters     3 United States US-specif… Number of Disast…
4  1983 United States Disasters     5 United States US-specif… Number of Disast…
5  1984 United States Disasters     2 United States US-specif… Number of Disast…
6  1985 United States Disasters     5 United States US-specif… Number of Disast…
# A tibble: 6 x 7
   Year Country      Indicator   Value Region      Type      Label              
  <dbl> <chr>        <chr>       <dbl> <chr>       <chr>     <chr>              
1  1895 United Stat… Temperature  50.3 United Sta… US-speci… Temperature (Fahre…
2  1896 United Stat… Temperature  52.0 United Sta… US-speci… Temperature (Fahre…
3  1897 United Stat… Temperature  51.6 United Sta… US-speci… Temperature (Fahre…
4  1898 United Stat… Temperature  51.4 United Sta… US-speci… Temperature (Fahre…
5  1899 United Stat… Temperature  51.0 United Sta… US-speci… Temperature (Fahre…
6  1900 United Stat… Temperature  52.8 United Sta… US-speci… Temperature (Fahre…

We can check the top and bottom of the new df_long tibble to see that our us_temperature data is at the bottom.

# A tibble: 6 x 7
  Country      Year Label                   Indicator Value Region         Type 
  <fct>       <dbl> <chr>                   <chr>     <dbl> <chr>          <chr>
1 Afghanistan  1751 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
2 Afghanistan  1751 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…
3 Afghanistan  1751 CO2 Emissions (Metric … Energy       NA Rest of the W… Glob…
4 Afghanistan  1751 CO2 Emissions (Metric … Deaths       NA Rest of the W… Glob…
5 Afghanistan  1752 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
6 Afghanistan  1752 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…
# A tibble: 6 x 7
  Country       Year Label                Indicator  Value Region      Type     
  <fct>        <dbl> <chr>                <chr>      <dbl> <chr>       <chr>    
1 United Stat…  2014 Temperature (Fahren… Temperatu…  52.5 United Sta… US-speci…
2 United Stat…  2015 Temperature (Fahren… Temperatu…  54.4 United Sta… US-speci…
3 United Stat…  2016 Temperature (Fahren… Temperatu…  54.9 United Sta… US-speci…
4 United Stat…  2017 Temperature (Fahren… Temperatu…  54.6 United Sta… US-speci…
5 United Stat…  2018 Temperature (Fahren… Temperatu…  53.5 United Sta… US-speci…
6 United Stat…  2019 Temperature (Fahren… Temperatu…  52.7 United Sta… US-speci…

Click here for details about the difference between full_join() and bind_rows()

The difference between this function and the full_join() function is that the bind_rows() function will assentially just append each dataset to each other, whereas the full_join() function collapses data that is comparable. Here you will see an example of what the data would have been like for df_wide if we had made it using bind_rows(). We will specifically look at the values for China in the year of 1960.

[1] 118478      7
[1] 118478      7
# A tibble: 4 x 7
  Country  Year Emissions Label                             GDP Energy Deaths
  <chr>   <dbl>     <dbl> <chr>                           <dbl>  <dbl>  <dbl>
1 China    1960    781000 CO2 Emissions (Metric Tons)     NA        NA   NA  
2 China    1960        NA GDP Growth/Capita (%)           -6.45     NA   NA  
3 China    1960        NA Energy Use (kg, oil-eq./capita) NA        NA   NA  
4 China    1960        NA Deaths/1000 People              NA        NA   25.4
# A tibble: 4 x 7
  Country  Year Emissions Label                             GDP Energy Deaths
  <chr>   <dbl>     <dbl> <chr>                           <dbl>  <dbl>  <dbl>
1 China    1960    781000 CO2 Emissions (Metric Tons)     NA        NA   NA  
2 China    1960        NA GDP Growth/Capita (%)           -6.45     NA   NA  
3 China    1960        NA Energy Use (kg, oil-eq./capita) NA        NA   NA  
4 China    1960        NA Deaths/1000 People              NA        NA   25.4

To remove entries for countries with NA values we can use filter(complete.cases(.)).

# A tibble: 6 x 7
  Country      Year Label                   Indicator Value Region         Type 
  <fct>       <dbl> <chr>                   <chr>     <dbl> <chr>          <chr>
1 Afghanistan  1751 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
2 Afghanistan  1751 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…
3 Afghanistan  1751 CO2 Emissions (Metric … Energy       NA Rest of the W… Glob…
4 Afghanistan  1751 CO2 Emissions (Metric … Deaths       NA Rest of the W… Glob…
5 Afghanistan  1752 CO2 Emissions (Metric … Emissions    NA Rest of the W… Glob…
6 Afghanistan  1752 CO2 Emissions (Metric … GDP          NA Rest of the W… Glob…
# A tibble: 6 x 7
  Country      Year Label                   Indicator Value Region         Type 
  <fct>       <dbl> <chr>                   <chr>     <dbl> <chr>          <chr>
1 Afghanistan  1949 CO2 Emissions (Metric … Emissions  14.7 Rest of the W… Glob…
2 Afghanistan  1950 CO2 Emissions (Metric … Emissions  84.3 Rest of the W… Glob…
3 Afghanistan  1951 CO2 Emissions (Metric … Emissions  91.7 Rest of the W… Glob…
4 Afghanistan  1952 CO2 Emissions (Metric … Emissions  91.7 Rest of the W… Glob…
5 Afghanistan  1953 CO2 Emissions (Metric … Emissions 106   Rest of the W… Glob…
6 Afghanistan  1954 CO2 Emissions (Metric … Emissions 106   Rest of the W… Glob…

Data Exploration

Now we will create some simple plots to examine the data.

We can check the time span of this data by refering back to the What are the data? section. To make these plots we will use the ggplot2 package. The first step in creating a plot is to define what data we intend to use and what data will be ploted on the x-axis, the y-axis, and if any data will be used to determine the color or the fill (also color of plots that have something to fill like a bar plot) or group. All of these are defined using the aes() argument, which is short for aesthetic mappings.

First we will take a look at the CO2 emission data.

CO2 Emissions (1751-2014)

We first need to give the correct data input. We will filter our data to only include the CO2 emissions data by using the filter() function of the dplyr package. To use this function we need to specify what value we want for a given variable. In this case we want all rows where the Indicator variable is equal to the word Emissions. Notice that this needs to be in quotes, while the variable name does not.

Then we use the aes() argument of the ggplot() function to define that our x-axis will be the Year variable, the y-axis will be the emission Value variable, and that our data should be grouped or separted by the Country variable. If we were to stop there we would get a blank plot, as you can see below. We need to add another layer to define how we want the plot to look. We do so by using the + sign in between each command.

We will use the geom_line() function becuase we would like to create a line plot. There are many geom_* functions to choose from that create many different types of plots.

Type geom into the RStudio console and you will see many options to scroll through.

Since we have many overlapping lines, we will make our lines slightly transparent by using the alpha argument. This takes values from 0 to 1, where 0 is completely transparent and 1 is completely opague. We will also add labels using the labs() function. Again, notice that a plus sign is used between each layer that we add to the plot. To make CO2 appear with a subscript we can use ~CO[2]~. We will also use the function theme_linedraw() of ggplot2 to change the general apearance of the plot.

Type theme_ in the RStudio console to see the varios plot theme options available.

We will also use the theme() function to change the font size of the x-axis, y-axis, axis titles, and the caption as shown below. To know what to call each element of the plot in this function to change the size type ?theme() in the console. You will see a very large list that includes other plot aspects like the background and the legend. This function can be used to modify your plot to your specifications.

Great! We’ve created our first plot. We can see that many countries show a dramatic increase in emissions over time with a handful of countries with particularly high levels.

Now let’s take a look at the data in slightly different way. Let’s look at overall global emissions by calculating a sum each year of all the emission values for the different countries. Note that this is limited to only the countries included in the dataset.

To calculate this value we will first use the group_by() function of the dplyr package. This will allow our calcluation to be performed on aggregated data by the different values for the Year variable. Otherwise, we would simply get a sum of overall emissions across all of the years in the data set.

Then we will use the summarize() function (also of the dplyr package) and the base sum() function to calculate a sum of the emission values each year.

Since we will be ploting only one value each year, we no longer need to assign a group in the aes() argument. this time we will make the size of the line that will be plotted a bit larger using the size() argument in the geom_line() function.

Ok, we can now clearly see that global CO2 emissions have dramatically risen since 1900.

Yearly Growth in GDP per Capita (1801 to 2019)

Now we will take a look a GDP growth of various countries

We can see that the variation in GDP has become greater over time.

Crude Mortality Rate

# A tibble: 244 x 7
   Country        Year Label              Indicator Value Region           Type 
   <fct>         <dbl> <chr>              <chr>     <dbl> <chr>            <chr>
 1 Cambodia       1980 Deaths/1000 People Deaths     43.9 Rest of the Wor… Glob…
 2 Timor-Leste    1980 Deaths/1000 People Deaths     26.7 Rest of the Wor… Glob…
 3 Niger          1980 Deaths/1000 People Deaths     26.0 Rest of the Wor… Glob…
 4 Mali           1980 Deaths/1000 People Deaths     25.2 Rest of the Wor… Glob…
 5 Sierra Leone   1980 Deaths/1000 People Deaths     24.8 Rest of the Wor… Glob…
 6 South Sudan    1980 Deaths/1000 People Deaths     24.6 Rest of the Wor… Glob…
 7 Guinea         1980 Deaths/1000 People Deaths     23.4 Rest of the Wor… Glob…
 8 Guinea-Bissau  1980 Deaths/1000 People Deaths     21.5 Rest of the Wor… Glob…
 9 Malawi         1980 Deaths/1000 People Deaths     21.5 Rest of the Wor… Glob…
10 Mozambique     1980 Deaths/1000 People Deaths     21.5 Rest of the Wor… Glob…
# … with 234 more rows
# A tibble: 248 x 7
   Country       Year Label              Indicator Value Region            Type 
   <fct>        <dbl> <chr>              <chr>     <dbl> <chr>             <chr>
 1 Rwanda        1995 Deaths/1000 People Deaths     34.4 Rest of the World Glob…
 2 Sierra Leone  1995 Deaths/1000 People Deaths     26.1 Rest of the World Glob…
 3 Niger         1995 Deaths/1000 People Deaths     20.2 Rest of the World Glob…
 4 Mali          1995 Deaths/1000 People Deaths     19.7 Rest of the World Glob…
 5 Angola        1995 Deaths/1000 People Deaths     19.3 Rest of the World Glob…
 6 Uganda        1995 Deaths/1000 People Deaths     18.8 Rest of the World Glob…
 7 Malawi        1995 Deaths/1000 People Deaths     18.5 Rest of the World Glob…
 8 Somalia       1995 Deaths/1000 People Deaths     18.4 Rest of the World Glob…
 9 Nigeria       1995 Deaths/1000 People Deaths     18.4 Rest of the World Glob…
10 Chad          1995 Deaths/1000 People Deaths     18.4 Rest of the World Glob…
# … with 238 more rows
# A tibble: 248 x 7
   Country    Year Label              Indicator Value Region            Type  
   <fct>     <dbl> <chr>              <chr>     <dbl> <chr>             <chr> 
 1 Bulgaria   2017 Deaths/1000 People Deaths     15.5 Rest of the World Global
 2 Latvia     2017 Deaths/1000 People Deaths     14.8 Rest of the World Global
 3 Serbia     2017 Deaths/1000 People Deaths     14.8 Rest of the World Global
 4 Lesotho    2017 Deaths/1000 People Deaths     14.7 Rest of the World Global
 5 Ukraine    2017 Deaths/1000 People Deaths     14.5 Rest of the World Global
 6 Lithuania  2017 Deaths/1000 People Deaths     14.2 Rest of the World Global
 7 Hungary    2017 Deaths/1000 People Deaths     13.5 Rest of the World Global
 8 Romania    2017 Deaths/1000 People Deaths     13.3 Rest of the World Global
 9 Croatia    2017 Deaths/1000 People Deaths     13   Rest of the World Global
10 Georgia    2017 Deaths/1000 People Deaths     12.9 Rest of the World Global
# … with 238 more rows

NULL

Data Visualization

Now Let’s try putting the data together.

This looks a bit awkward, because the eacy type of data spans a different time spans.

Time spans of data

Let’s take a look at the reporting countries for each year for each type of data.

We can see that all of our data spans from 1980 to 2014.

To color our plot we will use the viridis color pallette which is compatible with color-blindness by using the scale_fill_viridis_c() function which is simply available by loading the ggplot2 package.There are a few variations such as discreet as _d, or binned continuous as _b. The _c indicates a continuous scale. See here for more information.

Interestingly, we can see that Germany had ver low imission rates at the end of World War II. We see that the US has consistently had high emission rates since 1900, but that the emission rates in China recently surpased that of the US.

## US-specific


    Pearson's product-moment correlation

data:  pull(df_wide_US, Emissions) and pull(df_wide_US, Temperature)
t = 3.4815, df = 29, p-value = 0.0016
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.2334979 0.7524951
sample estimates:
      cor 
0.5429183 

    Pearson's product-moment correlation

data:  pull(df_wide_US, Emissions) and pull(df_wide_US, Disasters)
t = 2.8569, df = 29, p-value = 0.007833
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.1370685 0.7057842
sample estimates:
      cor 
0.4686469

---
title: "Open Case Studies : CO2 Emissions Over Time"
author: "Michael Ontiveros, Carrie Wright, PhD."
css: style.css
output:
  html_document:
    self_contained: yes
    code_download: yes
    highlight: tango
    number_sections: no
    theme: cosmo
    toc: yes
    toc_float: yes
  pdf_document:
    toc: yes
  word_document:
    toc: yes
---

<style>
#TOC {
  background: url("https://opencasestudies.github.io/img/logo.jpg");
  background-size: contain;
  padding-top: 240px !important;
  background-repeat: no-repeat;
}
</style>

```{r setup, include=FALSE}
knitr::opts_chunk$set(include = TRUE, comment = NA, echo = TRUE,
                      message = FALSE, warning = FALSE, cache = FALSE,
                      fig.align = "center", out.width = '90%')
library(here)
library(knitr)
```

#### {.outline }
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "mainplot.png"))
```

####

## {.disclaimer_block}

**Disclaimer**: The purpose of the [Open Case Studies](https://opencasestudies.github.io){target="_blank"} project is **to demonstrate the use of various data science methods, tools, and software in the context of messy, real-world data**. A given case study does not cover all aspects of the research process, is not claiming to be the most appropriate way to analyze a given data set, and should not be used in the context of making policy decisions without external consultation from scientific experts. 

# **Motivation**
*** 

This case study explores how different countries have contributed to Carbon Dioxide (CO2) emissions over time and how CO2 emission rates may relate to increasing global temperatures and increased rates of natural disasters and storms. This report provides a basis for the motivation: https://www.epa.gov/report-environment/greenhouse-gases.


CO2 makes up the largest proportion of greenhouse gas emissions in the United States:


```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "emissions.jpg"))
```

A variety of sources and sectors contribute to greenhouse gas emissions, with transportation contributing the most metric tons of CO2:


```{r, echo = FALSE, out.width="600px"}
knitr::include_graphics(here::here("img", "sector.png"))
```

So why should we pay attention to greenhouse gases?

According to the [US Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks 2020 Report](https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks): 

> Greenhouse gases absorb infrared radiation, thereby trapping heat in the atmosphere and making the planet warmer. The most important greenhouse gases directly emitted by humans include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and several fluorine-containing halogenated substances. Although CO2, CH4, and N2O occur naturally in the atmosphere, human activities have changed their atmospheric concentrations. From the pre- industrial era (i.e., ending about 1750) to 2018, concentrations of these greenhouse gases have increased globally by 46, 165, and 23 percent, respectively (IPCC 2013; NOAA/ESRL 2019a, 2019b, 2019c). 

* IPCC stands for the Intergovernmental Panel on Climate Change

There are many signs that our planet is experiencing warmer temperatures:

```{r, echo = FALSE, out.width="600px"}
knitr::include_graphics(here::here("img", "warming.png"))
```

The connection between greenhouse gas levels and global temperatures and the influence of increased global temperatures on human health are motivated by these reports:

#### {.reference_block}

Melillo, J.M., T.C. Richmond, and G.W. Yohe (eds.). 2014. Climate change impacts in the United States: The third National Climate Assessment. U.S. Global Change Research Program.  

2020. “Inventory of US Greenhouse Gas Emissions and Sinks: 1990--2018.” EPA 430-R-20-002, Tech. Rep. https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.


####

The National Climate Assessment Report states that:

> Heat-trapping gases already in the atmosphere have committed us to a hotter future with more climate-related impacts over the next few decades. The magnitude of climate change beyond the next few decades depends primarily on the amount of heat-trapping gases that human activities emit globally, now and in the future.

See [here](https://www.epa.gov/report-environment/greenhouse-gases) and [here](https://world101.cfr.org/global-era-issues/climate-change/climate-change-adaptations) for more information.

# **Main Questions**
*** 

#### {.main_question_block}
<b><u> Our main question: </u></b>

1) How have global CO2 emission rates changed over time? In particular for the US, and how does the US compare to other countries? 
2) Are US CO2 emissions, global temperatures, and US storm rates associated? 

####

# **Learning Objectives** 
*** 

In this case study, we will explore CO2 emission data from around the world. We will also focus on the US specifically to evaluate patterns of temperatures and storm activity. This case study will particularly focus on how to use different datasets that span different ranges of time, as well as how to create visualizations of patterns over time. We will especially focus on using packages and functions from the [`Tidyverse`](https://www.tidyverse.org/){target="_blank"}, such as `dplyr`, `tidyr`, `plotly`and `gganimate`. The tidyverse is a library of packages created by RStudio. While some students may be familiar with previous R programming packages, these packages make data science in R especially efficient.


*** 


We will begin by loading the packages that we will need:

```{r}
library(here)
library(readxl)
library(readr)
library(dplyr)
library(magrittr)
library(tidyverse)
library(plotly)
library(ggplot2)
library(gganimate)
library(ggrepel)
library(RColorBrewer)
```


 Package   | Use                                                                         
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"}       | to easily load and save data
[readxl](https://readxl.tidyverse.org/){target="_blank"}  | to import the excel file data
[readr](https://readr.tidyverse.org/){target="_blank"}  | to import the csv file data
[dplyr](https://dplyr.tidyverse.org/){target="_blank"}  |  to view and wrangle the data
[magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html){target="_blank"}  |  to use and reassign data objects using the `%<>%`pipe operator
[tidyverse](https://www.tidyverse.org/packages/){target="_blank"}  | to wrangle the data and create ggplot2 plots
[plotyly](https://plotly.com/r/){target="_blank"}  | to make the visualizations
[ggplot2](https://ggplot2.tidyverse.org/){target="_blank"} | to make visualizations
[ggrepel](https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html){target="_blank"} | to add labels that don't overlap to plots
[gganimate](https://gganimate.com/){target="_blank"}  | to make the plots interactive
[RColorBrewer](https://cran.r-project.org/web/packages/RColorBrewer/index.html){target="_blank"}  | to have greater control over the color in our plots

The first time we use a function, we will use the `::` to indicate which package we are using. Unless we have overlapping function names, this is not necessary, but we will include it here to be informative about where the functions we will use come from.


# **Context**
*** 

Greenhouse gas emissions are due to both natural processes and anthropogenic (human-derived) activities. 

These emissions are one of the contributing factors to rising global temperatures, which can have a great influence on [public health](https://www.epa.gov/climate-indicators/understanding-connections-between-climate-change-and-human-health){target="_blank"}  as illustrated in the following image:

```{r, echo = FALSE, out.width="800px"}
knitr::include_graphics(here::here("img", "health.png"))
```

> Gases in the atmosphere can contribute to climate change both directly and indirectly. Direct effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or albedo). The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of a greenhouse gas to trap heat in the atmosphere relative to another gas.
The GWP of a greenhouse gas is defined as the ratio of the accumulated radiative forcing within a specific time horizon caused by emitting 1 kilogram of the gas, relative to that of the reference gas CO2 (IPCC 2013). Therefore GWP-weighted emissions are provided in million metric tons of CO2 equivalent (MMT CO2 Eq.)


 CO2 is actually the least capable of the greenhouse gases for trapping heat:

```{r, echo = FALSE, out.width="800px"}
knitr::include_graphics(here::here("img", "GWP.png"))
```

However, because CO2 is so much more abundant and stays in the atmosphere so much longer than other greenhouse gases, it has been the largest contributor to global warming.

See [here](https://www.ucsusa.org/resources/why-does-co2-get-more-attention-other-gases#:~:text=CO2%20sticks%20around,oxide%20(N2O){target="_blank"}.)
for more details.


Furthermore, sizing CO2 levels also influence ocean acidity:

```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "oceans.png"))
```

This makes it difficult for organisms to maintain their shells or skeletons that are made of calcium carbonate, thus making it more difficult for these organisms to survive and impacting their role in the ecosystem and food chain. 


Furthermore, greenhouse gas emissions are believed to influence storm rates. 

Indeed events with high levels of precipitation which can induce flooding and property damage are generally increasing around the country:

```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "storms.png"))
```


# **Limitations**
*** 

There are some important considerations regarding this data analysis to keep in mind: 

1) The datasets included only include countries and years in which countries were reporting such information to the agencies that collected the data. Thus the data is incomplete. For example while we have a fairly good sense of CO2 emissions globally for later years, additional emissions were also produced by countries that are not included in the data.

2) [Correlation or association does not imply causation](https://dfrieds.com/math/correlation-does-not-imply-causation.html){target="_blank"}. We will be showing how different datasets show similar trends across time. This does not imply that one caused the other. However, in the case of some of the data we will show, there is additional scientific evidence to suggest that for example, increased CO2 emissions may cause increased temperatures or increased rates of disastors. However, simply showing a similar trend over time does not in itself prove that two variables are causally related. As you can see from this plot, often data may show a similar pattern over time by random chance.  See this [website](https://www.tylervigen.com/spurious-correlations){target="_blank"} for more examples.

```{r, echo = FALSE, out.width="500px"}
knitr::include_graphics(here::here("img", "causation.png"))
```




# **What are the data?**
*** 

In this case study we will be using data related to CO2 emissions, as well as other data that may influence, be influenced or relate to CO2 emissions. Most of our data was obtained from [Gapminder](https://www.gapminder.org/data/){target="_blank"}, which is a unique nonprofit that provides a variety of data for free.

In their words, Gapminder is...

> Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand.  Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found [here](https://www.gapminder.org/about-gapminder/constitution/).

The data that we will be using from Gapminder was obtained from the [World Bank](https://www.worldbank.org/en/what-we-do){target="_blank"}.


In addition we will use some data that is specific to the United States from the [National Oceanic and Atmospheric Administration (NOAA)] (https://www.noaa.gov/), which is an agency that collects weather and climate data.


Data   | Time span | Source  | Orginal Source   | Description | Citation                                                                    
---------- |-------------|-------------|-------------|--------|-------
**CO2 emissions**  |1751 to 2014 | [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [Carbon Dioxid Information Analysis Center (CDIAC)](https://cdiac.ess-dive.lbl.gov/){target="_blank"}  |  CO2 emissions in tonnes or metric tons (equivalent to approximately 2,204.6 pounds) per person by country| NA
**GDP per capita, percent yearly growth** | 1801 to 2019| [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG){target="_blank"}  |  [Growth Domestic Product](https://www.investopedia.com/terms/g/gdp.asp#:~:text=Gross%20Domestic%20Product%20(GDP)%20is%20the%20monetary%20value%20of%20all,expenditures%2C%20production%2C%20or%20incomes.){target="_blank"}  (which is an overall measure of the health of nation's economy) per person by country| NA
**Energy use per person** |1960 to 2015 | [Gapminder](https://www.gapminder.org/data/){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE){target="_blank"}  |  Use of primary energy before transformation to other end-use fules, by country | NA
**Crude Mortality Rate** |1960 to 2018 | [World Bank](https://data.worldbank.org/indicator/SP.DYN.CDRT.IN){target="_blank"}  | [World Bank](https://data.worldbank.org/indicator/SP.DYN.CDRT.IN){target="_blank"} |  Death rate per 1,000 people by country | NA 
**US Natural Disasters** | 1980 to 2019 | [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/billions/time-series){target="_blank"}| [The National Oceanic and Atmospheric Administration (NOAA) ](https://www.ncdc.noaa.gov/billions/time-series){target="_blank"}|  US data about: <br> -- Droughts <br> -- Floods <br> -- Freezes <br> -- Severe Storms <br> -- Tropical Cyclones <br> -- Wildfires<br> -- Winter Storms | NOAA National Centers for Environmental Information (NCEI) U.S. Billion-Dollar Weather and Climate Disasters (2020). https://www.ncdc.noaa.gov/billions/, DOI: 10.25921/stkw-7w73
**Temperature**  | 1895 to 2019|  [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/cag/national/time-series){target="_blank"}  | [The National Oceanic and Atmospheric Administration (NOAA)](https://www.ncdc.noaa.gov/cag/national/time-series){target="_blank"} | US National yearly average temperature (in Fahrenheit) from 1895 to 2019 | NOAA National Centers for Environmental information, Climate at a Glance: National Time Series, published June 2020, retrieved on June 26, 2020 from https://www.ncdc.noaa.gov/cag/


To obtain the temperature data, annual average temperatures were selected as shown in this image:
```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "temp.png"))
```


Importantly, notice that the data we would like to use span different time periods:

Data   | Time span                                                                     
---------- |-------------
**CO2 emissions**  |1751 to 2014 
**GDP per capita, yearly growth** | 1801 to 2019
**Energy use per person** |1960 to 2015 
**Crude Mortality Rate** |1960 to 2018 
**US Natural Disasters** | 1980 to 2019 
**Temperature**  | 1895 to 2019




# **Data Import**
*** 

To read in the files that were downloaded from the various sources as indicated in the table above, we will use the `read_xlsx()` and `read_xls()` functions of the `readxl` package to import the data from the .xlsx and .xls files respectively and we will use the `read_csv` function of the `readr` package to import the data from the csv files.

```{r}
# xlsx files:
CO2_emissions <- readxl::read_xlsx(here("docs/yearly_co2_emissions_1000_tonnes.xlsx"))
gdp_growth <- readxl::read_xlsx(here("docs/gdp_per_capita_yearly_growth.xlsx"))
energy_use <- readxl::read_xlsx(here("docs/energy_use_per_person.xlsx"))

# xls file:
mortality <- readxl::read_xls(here("docs/API_SP.DYN.CDRT.IN_DS2_en_excel_v2_804384.xls"))
```

For our csv data files, there are some lines that we would like to not import - infact, we will get an error if we try to import them because our table structure will be as r expects. We can do so using the `skip =` argument of the `read_csv()` function. 

Here you can see that the first two rows of the data about US Disasters doesn't have the same number of columns as the subsequent rows. So we want to skip these first two lines, we will use `skip = 2` for this.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "Disasters.png"))
```
Now looking at the temperature data, we can see that  the first four lines do not have the same number of columns as the subsequent lines. We will skip importing all 4 lines by using `skip = 4`. We can also specify that `NA` values are encoded as `"-99"`. This will replace all instances of `"-99"` with `NA`. We can do this using the `na = ` argument of the `read_csv()` function. We will do so as: `na = "-99"`. The "-99" needs to be in quotation markes becuase this argument expects characters.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "tempdata.png"))
```

```{r}
#csv files:
us_disaster <- readr::read_csv(here("docs/time-series-US.csv"), skip = 2)
us_temperature <- readr::read_csv(here("docs/temperature.csv"), skip = 4, na ="-99")
mortality2 <-readr::read_csv(here("docs/mortality.csv"), skip = 5)
```

Great! now we have imported all of the data that we will need.


# **Data Wrangling**
*** 

Now we will take a look at our data and wrangle it until it is easy to use to allow us to evaluate how CO2 emissions have changed over time and how emissions may relate to energy use, mortality, GDP etc. 

### Yearly CO~2~ Emissions

First let's take a look at the CO2 data. We can use the base `head()` function to see just the first 6 rows of our data.

We will use the `%>%` pipe which can be used to define the input for later sequential steps. This will make more sense when we have multiple sequential steps using the same data object. To use the pipe notation we need to install and load  the `dplyr` package.

```{r}
CO2_emissions %>%
  head()
```

OK, we can see that our country data makes of the rows and the yearly data makes up the columns. We also see that we have alot of `NA` values.


We can also use the `glimpse()` function of the `dplyr` packge to view our data. This allows us to see more of our data at once. We will see a tiny bit of each variable/column. To do so our data will be displayed with the column names listed on the right.

#### {.scrollable }
```{r}
# Scroll through the output!
CO2_emissions %>%
dplyr::glimpse()
```
####


  Indicator                           n
  <chr>                           <int>
1 CO2 Emissions (Mg)              57246
2 Deaths/1000 People              57246
3 Disasters                          40
4 Energy Use (kg, oil-eq./capita) 57246
5 GDP Growth/Capita (%)           57246
6 Temperature (Fahrenheit)          116


We can see that we have a large [tibble](https://tibble.tidyverse.org/). A tibble is the tidyverse version of a data frame. It is essentially a table with variable information arranged as columns, and individual observations arranged as rows. We can see that the tibble gives us information about the class of each variable.  For example the `country` variable is made up of character (abbreviated as chr) values. We see that we have 265 different country variables and CO2 emission values for 192 different years (from 1751 to 2014). Recall that the values are emissions in metric tons also called tonnes. We can see that there are fewer `NA` values for later years.

Now we will modify this data to make it more usable for making visualizations. One thing we will use is the `%<>%` opperator which is from the `magrittr` package. This allows us to use our `CO2_emissions` data and reassign it to a modified version at the same time. 

We will use the `pivot_longer()` function of the `dplyr` package to convert our data into what is called long format. This means that we will have more rows and fewer columns than our current format. This is done by collapsing multiple variables into fewer variables.

We want to collapse all of the values for the emission data across the different individual year variables into one new emission variable and we will identify what year they are from using a new `Year` variable.

```{r}
CO2_emissions  %<>%
  pivot_longer(cols = -country, names_to = "Year", values_to = "Emissions")

head(CO2_emissions)
```

We also want to rename the `country` variable to be capitalized. W We can use the `rename()` function of the `dplyr` package to rename this variable. When renaming variables the new name is listed first before the `=`. We will also modify the `Emissions` data by dividing it by 1000 to make the numbers smaller. To do this we will use the `mutate()` function, which is also part of the `dplyr()` package. This function allows us to create and modify variables. You may also note that the `Year` variable is currently of class type character. We would like to change it to be numeric. This can also be accomplished using the `mutate()` function.

```{r}
  
 CO2_emissions  %<>% 
   dplyr::rename(Country=country) %>%
#   dplyr::mutate(Emissions = Emissions/1000, 
         dplyr::mutate(Year = as.numeric(Year),
          Label = "CO2 Emissions (Metric Tons)")
     #rename(`CO2 Emissions (Mg)`= Emissions)

```

Now let's take a look to see how our data has changed:

```{r}

CO2_emissions %>%
tail()

```
Great, we can see that now the `Year` variable is of class double (abbreviated `dbl`), which is a numeric class.

### Yearly Growth in GDP per Capita

```{r}
gdp_growth %>%
  head()
```

```{r}
names(gdp_growth)
```

#### {.scrollable}
```{r}
# Scroll through the output!
gdp_growth %>%
glimpse()
```
####

Again, we will use the `pivot_longer()` to transform the data to long format. We will also again change the `country` variable to be `Country` by using the `rename()` function , and we will make the `Year` varaible numeric using the `mutate()` function. 

AVOCADO... this was in Michael's code... but I don't know that we need it
We will use the `drop_na()` function of the `tidyr` package to drop all years with missing data.

```{r}
# gdp_growth %<>%
#    tidyr::drop_na()
# 
# head(gdp_growth)
```

```{r}
gdp_growth %<>%
  pivot_longer(cols = -country, 
               names_to = "Year", 
               values_to = "gdp_growth") %>%
  rename(Country=country) %>%
  mutate(Year = as.numeric(Year),
         Label = "GDP Growth/Capita (%)") %>%
  #  tidyr::drop_na() %>% # not sure we need this..
  rename(GDP = gdp_growth)
```

Now let's see how this data has changed:

```{r}
gdp_growth %>%
  head()

gdp_growth %>%
  count(Year)
```

### Energy Use per Person

Now let's take a look at the energy use per person data:

```{r}
energy_use %>%
  head()
```

#### {.scrollable}
```{r}
energy_use %>%
  glimpse()
```
####

To wrangle the `energy_use` data, we will again convert the data to long format, rename some variables, and mutate the `Year` data to be numeric.

```{r}
energy_use %<>%
  pivot_longer(cols = -country, 
           names_to = "Year", 
          values_to = "energy_use") %>%
  rename(Country = country) %>%
  mutate(Year = as.numeric(Year),
         Label = "Energy Use (kg, oil-eq./capita)") %>%
  rename(Energy = energy_use)

```


```{r}
energy_use %>%
tail()
```


## Crude Mortality Rate

```{r}
mortality %>%
  head()
```


We can see that there are a couple of empty rows which indicate when the data was updated.
We can also see that the columns really start at the 3rd row. So first we will repace the column names with the 3rd row. Then we will remove the first 3 rows.

```{r}
colnames(mortality)
colnames(mortality) <- mortality[3,]
colnames(mortality)
mortality <- mortality[-c(1:3),]
```



#### {.scrollable}
```{r}
mortality %>%
  glimpse()

```
####


That is looking better! However, we also want to remove some variables like: `Country Code`, `Indicator Name`, and `Indicator Code`. We can do that using the `select()` functio of the `dplyr` package. We can use the minus sign `-` to indicate what variables we dont want to keep. Otherwise, we will perform similar modifications as we performed on the other datasets.

```{r}
mortality %<>%
  select(-`Country Code`,
         -`Indicator Name`,
         -`Indicator Code`) %>%
  rename(Country = "Country Name") %>%
  pivot_longer(cols = -Country, 
               names_to = "Year", 
               values_to = "Deaths") %>%
  mutate(Year = as.numeric(Year),
       Deaths = as.numeric(Deaths),
       Label = "Deaths/1000 People")

```

```{r}
mortality %>%
  head()
```


## US-specific Data

Now we will take a look at the US data about disasters and temperature.

### Disasters

```{r}
us_disaster  %>%
  head()
```

We are specifically interested in the `Year` and  the variables that contain the word `"Count"` so we will select them using the `select()` and `contains()` functions in the `dplyr` package. Since we are selecting for variables with the word `"Count"` we need to use quotation marks around it. Selecting for the variable `year` does not require this as that is actually the name of one of the existing variables.


```{r}
us_disaster %<>%
           select(Year, contains("Count"))

us_disaster %>%
  head()
```

Now we want to create a new variable that will be the sum of all the different types of disasters for each year. 

We can create this ne variable using the `mutate()` function of `dplyr` and we will use the base `rowSums()` function to perform the calculation. We dont want to include the `Year` variable in our sum, so we can exclude it using the `select`function within the `rowSums()` function. However, to do so we need to indicate that we are using the data that we already used as input to our `mutate()` and `rowSums()` functions. We can do so by using a `.`. 


```{r}
us_disaster %<>%
  mutate(`Disasters` = rowSums(select(., -Year))) 

us_disaster %>%
  glimpse()
```

Great, now we are going to remove some of these variables and just keep or select using the `select()` function the variables we are interested in. We will keep the `Flooding Count` becuase as you may recall from earlier in this case study, events of extreme perciptation levels appear to be associated with global warming. We will use this as a proxy for that.

AVOCADO why is US listed in 3 variables?
We are also going to add a new variable called `Country` to indicate that this data is from the United States. This will create a new variable where every value is `United States`. We will also create a new variable called `Region` where every value is `US-specific` and a new variable called `Type` where every value is `United States`.

```{r}
us_disaster %<>%
  dplyr::select(Year,
                Disasters) %>%
  mutate(Country = "United States") %>%
  pivot_longer(cols = c(-Country, - Year),
               names_to = "Indicator",
               values_to = "Value") %>%
  mutate(Region = "United States",
           Type = "US-specific",
          Label = "Number of Disasters")

head(us_disaster)
```



### Temperature

```{r}
us_temperature %>%
  head()
```
OK, so we want to remove the `Anomaly` variable which is an indicator of how different the national average temperature for that year was from the average temperature from 1901-2000 which was 52.02&deg;F. 

We also want to change the date values, which are currently listed as the year followed by the number 12. To do so we want to just keep the first 4 characters in the `Date` variable string values. We can use the `str_sub()` function of the `stringr` package to do this. We just need to indicate the start and stop characters. In this case the start would be 1 and the 4th character would be where we want to stop, so we would use `start = 1, stop = 4`. Again we will create a `Country`, `Region` and `Type` variable. We will also change the name of the `Date` variable to `Year` so that it will be consistent with our other datasets. Furthermore, we also what it to be numeric. We can accomplish both renaming and changing to numeric by using the `mutate()` function. We canthen remove the `Date` variable and also order the columns just like the other us data using the `select()` function.

```{r}
us_temperature %<>%
  dplyr::select(-Anomaly) %>%
  mutate(Date = str_sub(Date, start = 1, end = 4))%>%
  rename() %>%
  mutate(Year = as.numeric(Date), 
      Country = "United States",
    Indicator = "Temperature",
       Region = "United States",
         Type = "US-specific",
        Label = "Temperature (Fahrenheit)") %>%
  select(Year, Country, Indicator, Value, Region, Type, Label)

head(us_temperature)
```

## Joining data

Now we would like to join the different datasets together into one tibble. To do so it is often necessary to have at least one column or variable with the same name to be used as a key for putting your data together. To put all of our data together there are several `*_join()` functions available in the `dplyr` package. 


```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "join.png"))
```

We will use the `full_join()` function as we have different time spans for each dataset and we would like to retain as much data as possible. The`full_join()` function will simply create `NA` values for any of the years that are not in one of the data sets. We can check by using the base `summary()` function. This will also allow us to check that there are column names that are consistent in each dataset that we wish to combine.

```{r}
summary(CO2_emissions)
summary(gdp_growth)
summary(energy_use)
summary(mortality)

```

Indeed, `Country`, and `Year` variables are present in all of the datasets. We can see that the minimum and maximum year is different for nearly all the datasets.

We need to specify what columns/variables we will be joining by using the `by =` argument in the `full_join()` function.

```{r}

df_wide <- CO2_emissions %>%
  full_join(gdp_growth, by = c("Country", "Year", "Label")) %>%
  full_join(energy_use, by = c("Country", "Year", "Label")) %>%
  full_join(mortality, by = c("Country", "Year", "Label"))

df_wide %>%
  head()
```

We can also do the same thing using by using the`reduce()` function of the `purrr` package. This is a good option if you have many dasasets to combine.


```{r}
df_wide <- list(CO2_emissions, 
                gdp_growth, 
                energy_use, 
                mortality) %>% 
  reduce(full_join, by = c("Country", "Year", "Label"))

df_wide %>%
  head()
```

```{r}
df_wide %>%
  glimpse()
```

Nice, looks good!

We will also make a long version of this data, where we will create an new variable called `Indicator` that will indicate what dataset the data came from and we will collapse the values from the columns called ` Emissions` (`CO2 Emissions (Mg)`), `GDP`(`GDP Growth/Capita (%)`), `Energy`(`Energy Use (kg, oil-eq./capita)`), and `Deaths` (`Deaths/1000 People`). 


```{r}
df_long <- df_wide %>%
  pivot_longer(cols = c(-Country, -Year, - Label), 
               names_to = "Indicator", 
               values_to = "Value")
head(df_long)
```

We will also create a new variable called `Region` that will indicate if the data is about the United States or a different country based on the values in the `Country` variable. We will use the `case_when()` function of the `dplyr` package to do this. If the `Country` variable is equal to `"United States"` the value for the new variable will also be "United States", where as if the `Country` variable is not equal to `"United States"` but is some other character string value, such as `"Afghanistan"`, then the value for the new variable will be `"Rest of the World"`.
The new values for the new variable `Region` are indicated after the specific conditional statements by using the `~` symbol. We will also create a new variable called `Type`, where all the values are `"Global"` to indicate that this data is not specific to just the United States. 


```{r}
 df_long %<>%
  mutate(Region = case_when(Country == "United States" ~ "United States",
                            Country != "United States" ~ "Rest of the World"),
         Type = "Global")
head(df_long)
```

We will now combine this data with the US data about disasters and temperatures.

We will now use the `bind_rows()` function which will just append the `us_temperature` data and the `us_disaster` data after the `df_long` data. 


```{r}
head(us_disaster)
head(us_temperature)

df_long <-list(df_long, 
               us_disaster,
               us_temperature) %>%
  bind_rows()
df_long$Country <- as.factor(df_long$Country)
```

We can check the top and bottom of the new `df_long` tibble to see that our `us_temperature` data is at the bottom.

```{r}
head(df_long)
tail(df_long)
```


<details> <summary> Click here for details about the difference between `full_join()` and `bind_rows()` </summary>

The difference between this function and the `full_join()` function is that the `bind_rows()` function will assentially just append each dataset to each other, whereas the `full_join()` function collapses data that is comparable. Here you will see an example of what the data would have been like for `df_wide` if we had made it using `bind_rows()`. We will specifically look at the values for China in the year of 1960.

```{r}
df_wide_br <- list(CO2_emissions, 
                gdp_growth, 
                energy_use, 
                mortality) %>% 
  bind_rows()

dim(df_wide)
dim(df_wide_br)

df_wide%>%
  filter(Country == "China", Year == "1960")

df_wide_br%>%
  filter(Country == "China", Year == "1960")
```
</detials>


To remove entries for countries with NA values we can use `filter(complete.cases(.))`.
```{r}
df_long_with_miss <- df_long %>%
  arrange(Country)

df_long<- df_long %>%
  filter(complete.cases(.)) %>%
  arrange(Country)


head(df_long_with_miss)
head(df_long)

```




# **Data Exploration**
*** 
Now we will create some simple plots to examine the data.

We can check the time span of this data by refering back to the  [**What are the data?**] section. To make these plots we will use the `ggplot2` package. The first step in creating a plot is to define what data we intend to use and what data will be ploted on the x-axis, the y-axis, and if any data will be used to determine the color or the fill (also color of plots that have something to fill like a bar plot) or group. All of these are defined using the `aes()` argument, which is short for aesthetic mappings.

First we will take a look at the CO2 emission data.

## CO2 Emissions (1751-2014)

We first need to give the correct data input. We will filter our data to only include the CO2 emissions data by using the `filter()` function of the `dplyr` package. To use this function we need to specify what value we want for a given variable. In this case we want all rows where the `Indicator` variable is equal to the word `Emissions`. Notice that this needs to be in quotes, while the variable name does not.

Then we use the `aes()` argument of the `ggplot()` function to define that our x-axis will be the `Year` variable, the y-axis will be the emission `Value` variable, and that our data should be grouped or separted by the `Country` variable. If we were to stop there we would get a blank plot, as you can see below. We need to add another layer to define how we want the plot to look. We do so by using the `+` sign in between each command. 


```{r}
df_long %>%
  filter(Indicator == "Emissions") %>%
  ggplot(aes(x = Year, y = Value, group = Country))

```
We will use the `geom_line()` function becuase we would like to create a line plot. There are many `geom_*` functions to choose from that create many different types of plots.

Type `geom` into the RStudio console and you will see many options to scroll through.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "geom_.png"))
```
Since we have many overlapping lines, we will make our lines slightly transparent by using the `alpha` argument. This takes values from 0 to 1, where 0 is completely transparent and 1 is completely opague. We will also add labels using the `labs()` function. Again, notice that a plus sign is used between each layer that we add to the plot. To make CO2 appear with a subscript we can use `~CO[2]~`. We will also use the function `theme_linedraw()` of `ggplot2` to change the general apearance of the plot. 

Type `theme_` in the RStudio console to see the varios plot theme options available.

```{r, echo = FALSE, out.width = "800 px"}
knitr::include_graphics(here::here("img", "themes.png"))
```

We will also use the `theme()` function to change the font size of the x-axis, y-axis, axis titles, and the caption as shown below. To know what to call each element of the plot in this function to change the size type `?theme()` in the console. You will see a very large list that includes other plot aspects like the background and the legend. This function can be used to modify your plot to your specifications.

```{r}
df_long %>%
  filter(Indicator == "Emissions") %>%
  ggplot(aes(x = Year, y = Value, group = Country))+
  geom_line(alpha = 0.2) + 
  labs(title = "Country" ~CO[2]~ "Emissions per Year, 1751-2014",
     caption = "Limited to reporting countries",
           y = "Emissions (1M Metric Tons)") +
  geom_line(data = df_long %>%
  filter(Indicator == "Emissions",
         Country == "United States"), aes(x=Year, y=Value, color = Country)) +
  scale_colour_manual(values=c("red")) +
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16),
       legend.title = element_blank())
```

Great! We've created our first plot. We can see that many countries show a dramatic increase in emissions over time with a handful of countries with particularly high levels.

Now let's take a look at the data in slightly different way. Let's look at overall global emissions by calculating a sum each year of all the emission values for the different countries. Note that this is limited to only the countries included in the dataset. 

To calculate this value we will first use the `group_by()` function of the `dplyr` package. This will allow our calcluation to be performed on aggregated data by the different values for the `Year` variable. Otherwise, we would simply get a sum of overall emissions across all of the years in the data set.

Then we will use the `summarize()` function (also of the `dplyr` package) and the base `sum()` function to calculate a sum of the emission values each year.

Since we will be ploting only one value each year, we no longer need to assign a `group` in the `aes()` argument. this time we will make the size of the line that will be plotted a bit larger using the `size()` argument in the `geom_line()` function.

```{r}
CO2_world<-df_long %>%
  filter(Indicator == "Emissions") %>%
  group_by(Year) %>%
  summarize(Value = sum(Value)) %>%
  ggplot(aes(x = Year, y = Value)) +
  geom_line(size = 1.5) + 
  labs(title = "World "~CO[2]~" Emissions per Year , 1751-2014",
     caption = "Limited to reporting countries", 
           y = "Emissions (Metric Tonnes)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
CO2_world

```

Ok, we can now clearly see that global CO2 emissions have dramatically risen since 1900.

## Yearly Growth in GDP per Capita (1801 to 2019)

Now we will take a look a GDP growth of various countries

```{r}
df_long %>%
  filter(Indicator == "GDP") %>%
  ggplot(aes(x = Year, y = Value, group = Country)) +
  geom_line(alpha = 0.2) + 
  labs(title = "Country GDP Growth per Capita per Year (Annual %), 1801-2019",
     caption = "Limited to reporting countries",
           y = "GDP Growth per Capita (Annual %)") +
  geom_line(data = df_long %>%
  filter(Indicator == "GDP",
         Country == "United States"), aes(x=Year, y=Value, color = Country)) +
  scale_colour_manual(values=c("red")) +
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16),
       legend.title = element_blank())
```
We can see that the variation in GDP has become greater over time.

```{r}
df_long %>%
  filter(Indicator == "GDP",
         Year >= 1801) %>%
  group_by(Year) %>%
  summarise(Value = mean(Value, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  labs(title = "Mean Country GDP Growth per Capita per Year (Annual %), 1801-2019",
     caption = "Limited to reporting countries", 
           y = "GDP Growth per Capita (Annual %)") +
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
```

## Energy Use per Person (1960 to 2015)

```{r}
df_long %>%
  filter(Indicator == "Energy",
              Year >= 1960, 
              Year <= 2015) %>%
ggplot(aes(x=Year, y= Value, group=Country)) +
  geom_line(alpha=0.2) + 
  geom_line(data = df_long %>%
  filter(Indicator == "Energy",
         Country == "United States"), aes(x=Year, y=Value, color = Country)) +
  scale_colour_manual(values=c("red")) +
  labs(title = "Country Energy Use (kg of Oil Equivalent per Capita), 1960 to 2015",
     caption = "Limited to reporting countries", 
           y = "Energy Use (kg of Oil Equivalent per Capita)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 15),
       legend.title = element_blank())
```

```{r}
df_long %>%
  filter(Indicator == "Energy",
              Year >= 1960, 
              Year <= 2015) %>%
  group_by(Year) %>%
  summarise(Value = sum(Value, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  
  labs(title = "Worldwide Energy Use (kg of Oil Equivalent per Capita), 1960 to 2015",
     caption = "Limited to reporting countries",
           y = "Energy Use (kg of Oil Equivalent per Capita)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 15))
```

## Crude Mortality Rate


```{r}

Mortality <-df_long %>%
  filter(Indicator == "Deaths",
              Year >= 1960, 
              Year <= 2019) %>%
  ggplot(aes(x=Year, y=Value, group=Country)) +
  geom_line(alpha=0.2) + 
  geom_line(data =df_long %>%
  filter(Indicator == "Deaths",
         Country == "United States",
              Year >= 1960, 
              Year <= 2019), aes(x=Year, y=Value, color = Country)) +
  scale_colour_manual(values=c("red")) +
  labs(title = "Country Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16),
       legend.title = element_blank())

Mortality 
```
```{r}
df_long %>%
  filter(Indicator == "Deaths", Year == 1980) %>% arrange(-Value)

df_long %>%
  filter(Indicator == "Deaths", Year == 1995) %>% arrange(-Value)

df_long %>%
   filter(Indicator == "Deaths", Year == 2017) %>% arrange(-Value)
```

```{r}
df_long %>%
  filter(Indicator == "Deaths",
            Country == "Cambodia") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  labs(title = "Cambodia Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))

df_long %>%
  filter(Indicator == "Deaths",
            Country == "Rwanda") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  labs(title = "Rwanda Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))

df_long %>%
  filter(Indicator == "Deaths",
            Country %in% c("United States", "Rwanda", "Cambodia", "Qatar", "Niger", "Israel", "Mexico", "China"))%>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() + 
  facet_grid(~ Country)
  labs(title = " Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))


df_long %>%
  filter(Indicator == "Deaths",
            Country %in% c("United States", "Rwanda", "Cambodia", "Qatar", "Niger", "Israel", "Mexico", "China"))%>%
  ggplot(aes(x=Year, y=Value, color = Country)) +
  geom_line() + 
  labs(title = " Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))


df_long %>%
  filter(Indicator == "Deaths",
            Country %in% c("United States", "Rwanda", "Cambodia", "Qatar", "Niger", "Israel", "Mexico", "China"))%>%
    filter(Year > 2015) %>%
  ggplot(aes(x=Year, y=Value, color = Country)) +
  geom_line() + 
  labs(title = " Crude Mortality Rate (per 1000 Persons), 1960 to 2019",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
  
  
  
```

```{r}

df_long %>%
  filter(Indicator == "Deaths",
              Year >= 1960, 
              Year <= 2019) %>%
   group_by(Year) %>%
  summarise(Value = mean(Value, na.rm = TRUE)) %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line(size = 1.4) + 
  labs(title = "Mean Country Crude Mortality Rate (per 1000 Persons), 1960 to 2018",
     caption = "Limited to reporting countries",
           y = "Crude Mortality Rate (per 1000 Persons)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
  
```

## US Disasters

```{r}

df_long %>%
  filter(Indicator == "Disasters",
              Year >= 1980, 
              Year <= 2019) %>%
  ggplot(aes(x=Year, y=Value, group=Country)) +
  geom_line() + 
  geom_smooth(method = "loess") +
  labs(title = "US Disasters, 1980 to 2019",
    subtitle = "Drougths, Floods, Freezes, Severe Storms. Tropical Cyclones, Wildfires, and Winter Storms", 
           y = "Disaster Count")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
         plot.title = element_text(size = 16))
  
```

## US Temperature

```{r}

df_long %>%
  filter(Indicator == "Temperature",
              Year >= 1895, 
              Year <= 2019) %>%
  ggplot(aes(x=Year, y=Value, group=Country)) +
  geom_line() + 
  geom_smooth(method = "loess") +
  labs(title = "US Average Annual Temperature, 1895 to 2019",
           y =  "Temperature (Fahrenheit)")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
         plot.title = element_text(size = 16))

```


# **Data Visualization**
*** 

Now Let's try putting the data together. 

```{r,fig.width=10, fig.height=10}
ggplot(df_long, aes(x=Year, y=Value, group=Country)) +
  geom_line(alpha=0.2) + 
  geom_line(data = df_long %>%
  filter(Country == "United States"), aes(x=Year, y=Value, color = Country)) +
  scale_colour_manual(values=c("red")) +
  facet_wrap(Indicator~., scales = "free_y",strip.position="right", ncol = 1) +
  labs(title="Distribution of Indicators by Year and Value", 
       y = "Indicator Value")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
```

This looks a bit awkward, because the eacy type of data spans a different time spans.

## Time spans of data

Let's take a look at the reporting countries for each year for each type of data.

```{r}
df_long %>%
  filter(Type == "Global") %>%
  group_by(Year, Label, .drop=FALSE) %>%
  tally() %>%
  ggplot(aes(x= Year, y = n, color = Label)) +
  geom_line() +
    geom_vline(xintercept = 1980, linetype=2, color="black") +
   geom_vline(xintercept = 2014, linetype=2, color="black") +
  labs(title = "Countries with Complete Data per Year",
    subtitle = "Global Data", 
           y = "Countries") + 
  scale_x_continuous(breaks = seq(1750,2020,by=10),
                     labels = seq(1750,2020,by=10)) +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x = element_blank(),
        legend.position = "bottom")+
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12, angle = 90),
        axis.text.y = element_text(size = 12),
       axis.title.x = element_text(size = 12),
       axis.title.y = element_text(size = 12),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16),
       legend.title = element_blank())
```

We can see that all of our data spans from 1980 to 2014.

```{r}
df_long %>%
  filter(Region=="United States") %>%
  group_by(Label) %>%
  summarise(Start=min(Year), End=max(Year)) %>%
  ggplot(aes(x=Label, y=End)) +
  geom_hline(yintercept = 1980, linetype=2, color="black") +
  geom_hline(yintercept = 2014, linetype=2, color="black") +
  geom_segment(aes(x=Label,
                   xend=Label,
                   yend=End,
                   y=Start)) +
  geom_point(aes(x=Label, y=Start), shape=16, color="black") +
  geom_point(aes(x=Label, y=End), shape=21, fill="white", color="black") + 
  coord_flip() +
  labs(title = "Complete Data per Year",
    subtitle = "US-specific Data", 
           y = "Countries") + 
  scale_y_continuous(breaks = seq(1750,2020,by=10),
                     labels = seq(1750,2020,by=10)) +
  theme_linedraw() +
  theme(axis.text.x = element_text(size = 12,angle = 90),
        axis.text.y = element_text(size = 12),
         axis.title = element_blank(),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16))
```

```{r, Animation_1, warning=FALSE, eval=TRUE}
animation_1 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Deaths") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region,alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title = "Distribution of Indicators by Year and Value, 1980-2010",
           y = "Crude Mortality Rate") +
  theme(axis.text.x = element_text(angle = 90)) + 
  theme_classic() +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_1, fps = 10, duration = 5)
```

```{r, Animation_2, warning=FALSE, eval=FALSE}
animation_2 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Energy") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.5, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010",
       y = "Energy Use per Capita") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) + 
  
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_2, fps = 10, duration = 5)
```

```{r, Animation_3, warning=FALSE, eval=FALSE}
animation_3 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="GDP") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title="Distribution of Indicators by Year and Value, 1980-2010",
       y= "GDP Growth per Capita (%)") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) +
  
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_3, fps = 10, duration = 5)
```

```{r, Animation_4, warning=FALSE, eval=FALSE}
animation_4 <- df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Emissions") %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  ggplot(aes(x=Year, y=Value, group=Country, color=Region, size=Region, alpha=Region)) +
  geom_point() +
  scale_color_manual(values = c("Red","Black")) +
  scale_alpha_manual(values = c(0.1, 1)) +
  scale_size_manual(values = c(0.25, 2)) +
  labs(title = "Distribution of Indicators by Year and Value, 1980-2010",
           y = "CO2 Emissions (Mg)") +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90)) +
  transition_time(as.integer(Year)) +
  shadow_wake(wake_length = 1, alpha = FALSE)

animate(animation_4, fps = 10, duration = 5)
```

To color our plot we will use the viridis color pallette which is compatible with color-blindness by using the `scale_fill_viridis_c()` function which is simply available by loading the `ggplot2` package.There are a few variations such as discreet as `_d`, or binned continuous as `_b`. The `_c` indicates a continuous scale. See [here](https://ggplot2.tidyverse.org/reference/scale_viridis.html) for more information.

```{r}
Top10<-df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Emissions") %>%
  filter(Year>=1900) %>%
  group_by(Country) %>%
  mutate(max_val = max(Value)) %>%
  ungroup() %>%
  mutate(rank=dense_rank(-max_val)) %>%
  filter(rank<=10) %>%
  ggplot(aes(x=Year, y=fct_reorder(Country, Value, max))) +
  geom_tile(color="transparent", aes(fill=log(Value))) +
  scale_fill_viridis_c()+
  scale_x_continuous(breaks = seq(1900,2014,by=5),
                     labels = seq(1900,2014,by=5)) + 
  labs(title = "Top 10 "~CO[2]~"Emission-producing Countries in 2014",
    subtitle = "Ordered by Emissions Produced in 2014",
        fill = "Ln(CO2 Emissions (Mg))") +
  theme_classic() +
  theme(axis.text.x = element_text(size = 12, angle = 90),
        axis.text.y = element_text(size = 12),
         axis.title = element_blank(),
       plot.caption = element_text(size = 12),
         plot.title = element_text(size = 16),
    legend.position = "bottom")

Top10
```
Interestingly, we can see that Germany had ver low imission rates at the end of World War II. We see that the US has consistently had high emission rates since 1900, but that the emission rates in China recently surpased that of the US.

```{r}

Top10b<-df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Emissions") %>%
  filter(Year>=1900) %>%
  filter(Year<=2014) %>%
  group_by(Country) %>%
  mutate(max_val = max(Value)) %>%
  ungroup() %>%
  mutate(rank=dense_rank(-max_val)) %>%
  filter(rank<=10) %>%
  ggplot(aes(x=Year, y=Value, color = Country)) +
  geom_line() +
  scale_color_viridis_d()+
  geom_text_repel(data = df_long %>%
  filter(Type=="Global") %>%
  filter(Indicator=="Emissions") %>%
  filter(Year>=1900) %>%
  filter(Year<=2014) %>%
  group_by(Country) %>%
  mutate(max_val = max(Value)) %>%
  ungroup() %>%
  mutate(rank=dense_rank(-max_val)) %>%
  filter(rank<=10) %>%
              filter(Year == last(Year)),
            aes(label = Country,
                x = Year,
                y = Value),
            size = 3,
            alpha = 1,
            nudge_x = 10,
            direction = "y",
            hjust = 1,
            vjust = 1,
            segment.size = 0.25,
            segment.alpha = 0.25,
            force = 1)+
  theme_linedraw() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank(),
        legend.position = "none") + 
  labs(title = "Top 10 Emissions-producing Countries in 2010 (1900-2014)",
       subtitle = "Ordered by Emissions Produced in 2014",
       fill = "Ln(CO2 Emissions (Mg))")

Top10b


```
## US-specific

```{r}
df_long_us <- df_long %>%
  filter(Country=="United States")

# Approximated derivative function)
df_long_us <- df_long_us %>%
  filter(Year >= 1900,
         Year <= 2010) %>%
  group_by(Indicator) %>%
  mutate("Change (%)"=((Value/lag(Value))*100)-100,
         Mean=mean(Value),
         Anomaly=Value-Mean) %>%
  ungroup() %>%
  mutate(Anomaly_color=ifelse(Anomaly>0,"Positive",
                              ifelse(Anomaly<0,"Negative","Zero")),
         Anomaly_color=factor(Anomaly_color, levels = c("Positive",
                                                        "Negative",
                                                        "Zero"),
                              ordered = TRUE))
```

```{r}


US_Indicators <- df_long %>%
  filter(Country=="United States")%>%
  filter(Year>=1980) %>%
  ggplot(aes(x=Year, y=Value)) + 
  geom_line() + 
  geom_smooth(method = "loess") +
  facet_wrap(Label~., ncol=2, nrow=3, scales = "free_y") + 
  theme_linedraw() + 
  theme(axis.text.x = element_text(angle = 90, size = 12),
        axis.text.y = element_text(size = 12),
        strip.text.x = element_text(face = "bold", size = 12),
        axis.title.y = element_blank(),
        axis.title.x = element_text(size = 12)) + 
  labs(title = "US-specific Indicators")
US_Indicators
```

```{r}
df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2014) %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator, fill="transparent")) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 1, linetype=3) +
  geom_line(size=0.5) + 
  scale_x_continuous(breaks = seq(1980,2014,by=5),
                     labels = seq(1980,2014,by=5)) +
  scale_y_continuous(breaks = seq(-500,1250, by=250),
                     labels = seq(-500,1250, by=250)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US-specific Indicators (1980-2010)",
       subtitle = "Change (%) Lines")

df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2014) %>%
  filter(Indicator=="Temperature"|
           Indicator=="Energy"|
           Indicator=="Emissions") %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 1, linetype=3) +
  geom_line(size=1) +
  scale_x_continuous(breaks = seq(1980,2014,by=5),
                     labels = seq(1980,2014,by=5)) +
  scale_y_continuous(breaks = seq(-10,10, by=1),
                     labels = seq(-10,10, by=1),
                     limits = c(-10,10)) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "Emissions, Energy Use, and Temperature (1980-2010)",
       subtitle = "Change (%) Lines")

df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  filter(Indicator=="Temperature"|
           Indicator=="Energy"|
           Indicator=="Emissions") %>%
  ggplot(aes(x=Year, y=`Change (%)`, color=Indicator)) + 
  geom_hline(yintercept=0.8, linetype=2) +
  geom_hline(yintercept=1.2, linetype=2) +
  geom_hline(yintercept = 0, linetype=3) +
  geom_smooth(size=1, alpha=0.1, aes(fill=Indicator), se=TRUE) +
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) +
  scale_y_continuous(breaks = seq(-10,10, by=1),
                     labels = seq(-10,10, by=1),
                     limits = c(-10,10)) +
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
       subtitle = "Smoothed Change (%) Lines")
```

```{r}
df_long_us %>%
  filter(Year>=1980) %>%
  filter(Year<=2010) %>%
  filter(Indicator=="Emissions"|
           Indicator=="Temperature") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_line() +
  geom_smooth(method = "loess")+
  scale_x_continuous(breaks = seq(1980,2010,by=5),
                     labels = seq(1980,2010,by=5)) + 
  facet_wrap(Indicator~., scales = "free_y", ncol=1) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank()) + 
  labs(title="US Emissions and Temperatures (1980-2010)")
```


```{r}

df_wide_US <-df_long_us %>%
  select(-c(Label, Anomaly, Anomaly_color, Mean, "Change (%)", Type)) %>%
  pivot_wider(names_from = "Indicator", values_from = Value)%>%
  filter(Year>=1980) %>%
  filter(Year<=2010)
  
cor.test(pull(df_wide_US, Emissions),
         pull(df_wide_US, Temperature))

cor.test(pull(df_wide_US, Emissions),
         pull(df_wide_US, Disasters))

```


```{r}
df_long_us %>%
  filter(Indicator=="Emissions"|
           Indicator=="Temperature") %>%
  ggplot(aes(x=Year, y=Value)) +
  geom_vline(xintercept = 1980, linetype=2, color="black") + 
  geom_vline(xintercept = 2010, linetype=2, color="black") +
  geom_segment(aes(x=Year, y=Value, xend=Year, yend=Mean,color=Anomaly_color), size=1.25) +
  scale_color_manual(values = c("red","blue","gray")) + 
  geom_hline(aes(yintercept=Mean), linetype=1, color="black") +
  scale_x_continuous(breaks = seq(1900,2010,by=5),
                     labels = seq(1900,2010,by=5)) +
  facet_wrap(Indicator~., scales = "free_y", ncol=1) + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90),
        axis.title = element_blank(),
        legend.position = "none")  +
  labs(title = "US Emissions and Temperatures (1900-2010)",
       subtitle = "Indicator Mean Represented by Solid Black Line")
```

```{r}
df_long_us %>%
  filter(Indicator == "Temperature"|
         Indicator == "Emissions") %>%
  ggplot(aes(x=Year, y=`Change (%)`)) +
  annotate("rect", xmin=-Inf, xmax=Inf, ymin=0, ymax=Inf, alpha=0.25, fill="green") +
  annotate("rect", xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=0, alpha=0.25, fill="red") +
  geom_hline(yintercept=0, linetype=1) + 
  geom_segment(aes(x=Year, y=`Change (%)`, xend=Year, yend=0), size=1.25) +
  facet_wrap(Indicator~., scales = "free_y", ncol=1) +
  scale_x_continuous(breaks = seq(1900,2010,by=5),
                     labels = seq(1900,2010,by=5)) +
  theme_classic() + 
  theme(axis.text.x = element_text(angle = 90),
        axis.title.x  = element_blank(),
        legend.position = "bottom",
        legend.direction = "horizontal") + 
  labs(title = "US Emissions, Energy Use, and Temperatures (1980-2010)",
       subtitle = "Change (%) Lines")
```

## Main plot

```{r}

library(patchwork)

CO2_world + Top10 + US_Indicators +
  plot_layout(widths = c(1, 2), heights = unit(c(2, 5), c('cm', 'null')))

png(here::here("img", "mainplot.png"), width = 900, height = 700)
(CO2_world | Top10)/ US_Indicators+
    plot_layout(widths = c(1, 2), heights = unit(c(4, 5), c('cm', 'null')))
dev.off()
```


